Концептуальна модель та система для заміни тексту на зображенні зі збереженням стилю
Text replacement in images, particularly while preserving its style, is a complex task that requires solving a range of scientific challenges and developing new technical solutions. One of the main issues is maintaining the authenticity and harmony of the image after modifications. The Research Obje...
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| author | Maslianko, Pavlo Romanov, Mykola |
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| description | Text replacement in images, particularly while preserving its style, is a complex task that requires solving a range of scientific challenges and developing new technical solutions. One of the main issues is maintaining the authenticity and harmony of the image after modifications. The Research Objective is the development of a conceptual model and a system for text replacement in images with style preservation based on systems engineering methodology and the Eriksson-Penker business profile, ensuring the natural integration of new text elements into the image’s context. Implementation Methodology – the systems engineering methodology and the Eriksson-Penker business profile are used to formalize the structured process of developing a system for text replacement in images with style preservation. Research Results – a method for developing the system based on systems engineering techniques was proposed, consisting of four main stages. In the first stage, the system structure is modeled as an Eriksson-Penker business profile. In the second stage, a set of processes is defined that are characteristic of the Data Science system class and the CRISP-DM international standard. Also, the structural and dynamic representations of the conceptual model, as well as the component interaction interfaces, are modeled. The third stage involves implementing a specific version of the system, while the fourth stage focuses on system verification and validation. A systems engineering method for the conceptual model and system for text replacement in images with style preservation has been proposed. It is based on a modified Eriksson-Penker business profile for metalevel system representation and international standards for Data Science and Data Mining processes. |
| doi_str_mv | 10.20535/SRIT.2308-8893.2025.2.04 |
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Publisher IASA at the Igor Sikorsky Kyiv Polytechnic Institute, 2025
Системні дослідження та інформаційні технології, 2025, № 2 61
UDC 004.6:33
DOI: 10.20535/SRIT.2308-8893.2025.2.04
A CONCEPTUAL MODEL AND A SYSTEM FOR REPLACING
TEXT IN AN IMAGE WHILE PRESERVING THE STYLE
P. MASLIANKO, M. ROMANOV
Abstract. Text replacement in images, particularly while preserving its style, is a
complex task that requires solving a range of scientific challenges and developing
new technical solutions. One of the main issues is maintaining the authenticity and
harmony of the image after modifications. The Research Objective is the
development of a conceptual model and a system for text replacement in images
with style preservation based on systems engineering methodology and the
Eriksson-Penker business profile, ensuring the natural integration of new text
elements into the image’s context. Implementation Methodology – the systems
engineering methodology and the Eriksson-Penker business profile are used to
formalize the structured process of developing a system for text replacement in
images with style preservation. Research Results – a method for developing the
system based on systems engineering techniques was proposed, consisting of four
main stages. In the first stage, the system structure is modeled as an Eriksson-Penker
business profile. In the second stage, a set of processes is defined that are
characteristic of the Data Science system class and the CRISP-DM international
standard. Also, the structural and dynamic representations of the conceptual model,
as well as the component interaction interfaces, are modeled. The third stage
involves implementing a specific version of the system, while the fourth stage
focuses on system verification and validation. A systems engineering method for the
conceptual model and system for text replacement in images with style preservation
has been proposed. It is based on a modified Eriksson-Penker business profile for
metalevel system representation and international standards for Data Science and
Data Mining processes.
Keywords: systems engineering method, Eriksson-Penker business profile, concep-
tual model, system for text replacement in images with style preservation.
BACKGROUND
Replacing text in an image, in particular, while preserving its style, is a complex
task that requires solving a number of scientific problems and new technical solu-
tions. One of the main challenges is to preserve the authenticity and harmony of
the image after the changes have been made, as any modification of the text can
disrupt the original style, fonts, colours and visual composition. To make such
changes look natural, a mechanism is needed that can effectively identify the text
style, recreate it, and adapt it to the new content.
Key aspects of the task:
1. Preserving the style: when replacing text, it is important not only to cor-
rectly reproduce the font, size, colour and other visual characteristics, but also to
integrate it into the overall visual aesthetics of the image (including background,
shadows, textures).
2. Adaptability to different styles: texts on images can be executed in a wide
variety of styles — from handwritten text to complex graphic elements. The sys-
P. Maslianko, M. Romanov
ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 62
tem should be able to work with different text styles and formats, automatically
detecting them.
3. Contextual replacement: Often, text is not just a separate element, but is
integrated into a complex visual scene. Therefore, the replacement should take
into account not only the style but also the context of the image to make the new
text look appropriate.
4. Automation and scalability: manual text replacement is a time-consuming
process in many cases. Automation of this task will significantly save time with a
large volume of images, which is important for industries such as marketing, de-
sign or content localisation (Table 1).
T a b l e 1 . Overview and comparative analysis of existing solutions
Approach Advantages Disadvantages
Manual tools High precision at the right
skill level, full user control Labour intensive, difficult to scale
Classic computer
vision algorithms
Speed, automation
of key processes
Limited ability to save complex text
styles, need for additional customisation
Generative
models (GAN)
Ability to automatically save
complex stylistic elements of text
High cost of computing, dependence
on data quality, long learning curve
Current methods for style-preserving text replacement in images are mostly
based on generative adversarial networks and deep learning. StyleGAN remains
one of the most effective solutions, thanks to its ability to accurately reproduce
stylistic elements of text and integrate them into the overall context of the image.
Motivation for the development of the conceptual model and system to
replace text in an image while preserving the style.
The main motivation is the need for a system that will automatically replace
text in an image while maintaining its style. This is necessary for such cases of
activity:
1. Content localisation: Many companies and brands need to adapt their
visuals for different markets and languages. For example, advertising banners or
posters may have text that needs to be translated and replaced in other languages
without losing style and graphic authenticity.
2. Graphic design: Quickly changing text on design mockups can be impor-
tant when working on prototypes or changes during the approval phase of projects.
Thus, the relevance of the problem lies in the need to develop a scientifically
grounded conceptual model of this class of systems and implement an automated
tool for replacing text in images while preserving the style. The solution to this
problem opens up opportunities to speed up the content creation process, improve
its quality and accuracy of visual adaptation for various purposes.
PROBLEM STATEMENT
Object of research. Conceptual ideas and approaches, existing mathematical
models and algorithms for detecting, analysing and synthesising text in images,
their theoretical aspects, architecture and existing software tools for image and
text processing.
Subject of research. Conceptual model and system of text replacement in
an image with style preservation. Structural and dynamic representation of the
system, image generation algorithms. Models and algorithms for style control in
the process of image generation.
A conceptual model and a system for replacing text in an image while preserving…
Системні дослідження та інформаційні технології, 2025, № 2 63
Research objective. To develop a conceptual model and a system for
replacing text in images while preserving its style, which allows to accurately
identify, analyse and reproduce the visual characteristics of the text (font, size,
colour, orientation, textures, shadows and other stylistic elements), ensuring the
natural integration of new text elements into the context of the image.
Final result. A conceptual model and a system for replacing text in an im-
age while preserving its style, as well as verification and validation results.
A method of system engineering of a conceptual model and a system for re-
placing text on images while preserving its style
The main idea behind the development of the conceptual model is based on [1–4]
and consists in applying the systems engineering methodology and the Eriksson–
Penker business profile to formalise an orderly way of developing a text-to-
picture image replacement system with style preservation (Fig. 1).
One of the most common models of activity representation is the Eriksson–
Penker business profile [5–8], in the context of which the authors formulated four
main essences of the formal representation of the activity of any business system
(Fig. 1):
– goals (represent the purpose of the system and are formulated as a rule.
Goals can be broken down into sub-goals and achieved through the implementa-
tion of processes);
– processes (the main actions that make up the system’s activities and are
intended to achieve the goal in accordance with the established business rules.
Processes are usually subject to rules, can change the state of input resources, and
produce new resources — the system’s output resources — in accordance with
the conditions and requirements set by stakeholders);
– resources (physical, abstract or informational objects that the system con-
sumes, uses, processes and produces throughout its activities to achieve the goal);
– rules (certain formalised restrictions, frameworks, conditions and re-
quirements, etc. that are imposed on processes and describe the nature of the rela-
tionships between resources).
Such an ordered set of formalised (in particular, in UML notation) entities
and system representations based on the Eriksson–Penker business profile
formalises and systemises the conceptual model (meta-model) of a text-to-picture
image replacement system with style preservation (Fig. 1).
Thus, the essence of the system engineering method of the conceptual model
of the image text replacement system with style preservation is to apply the
system approach and the Eriksson–Penker business profile to formalise and
produce such systems
At the second stage, we formalise the structural and dynamic representation
of the conceptual model of the system, interfaces for component interaction,
technical requirements and specifications of all stakeholders.
At the third stage, we implement a specific version of the system based on
technological and mathematical tools and in accordance with the technical
requirements and specifications of all stakeholders.
The fourth stage involves verification and validation of a specific version of
the system.
P. Maslianko, M. Romanov
ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 64
Formalisation of Eriksson–Penker business profile classes for a conceptual
model and a system for replacing text on images while preserving its style
Let’s define the content of each of the classes of the diagram (Fig. 1) in terms of
the system engineering problem, namely classes:
1. Problem (an actual issue that requires appropriate solutions, the main mo-
tivation for developing a system that leads to the formulation of a specific goal.
The problem of this work: the need to develop a conceptual model and an auto-
mated tool for replacing text in images while preserving the style. The solution to
this problem opens up opportunities for significantly speeding up the content
creation process, improving its quality and accuracy of visual adaptation for vari-
ous purposes).
2. Purpose (expresses the global goal of the work designed to solve the
problem. The purpose of this work: To develop a system for automatically replac-
ing text in images while preserving its style, which allows for accurate identifica-
tion, analysis and reproduction of visual text characteristics (font, size, colour,
orientation, textures, shadows and other stylistic elements), ensuring natural inte-
gration of new text elements into the image context).
3. Process (a set of processes of the system’s activity, which results in
achieving the goal, a clearly defined sequence of actions/subprocesses that leads
to the fulfilment of a certain task. The processes of this system are: Loading train-
ing data, Pre-processing training data, Training the model, Replacing text in an
image with preservation of style, Functioning of the web application).
4. State change (possible changes in certain resources as a result of the proc-
esses. The system has three state changes: Image with text and word coordinate
file → Segmented images (the Pre-process training data process), Segmented im-
ages with text labels → Transformed images and text labels (the Replace text in
image with preservation of style process), Initialised model → Trained model (the
Train model process)).
Fig. 1. Improved Eriksson–Penker business profile. Class diagram in UML notation [1]
A conceptual model and a system for replacing text in an image while preserving…
Системні дослідження та інформаційні технології, 2025, № 2 65
5. Resource (any entities (tangible or intangible) that are consumed and pro-
duced by the system under development.
The resources of the lowest level of the hierarchy, which are directly in-
volved in the processes, are also divided into the following three classes by the
nature of their influence on the processes:
– Business process output (resources produced by the system, the end result
of its operation. This includes the generated image with new text).
– Business process support resource (resources that support the execution of
processes, but are not the final result of work: Trained model, Computer hardware
and computing resources, Pre-processing algorithms, Metadata files, Software).
– Business process input (primary input resources of the initial processes
that initialise the system cycle: Image with text, Metadata file for segmentation,
Image for text replacement, New text).
6. Event (occurs due to certain external factors or as a result of interaction
between processes. The potential events of this system are Uploading an image
with text by a user, Entering a new text to replace it, Loading training data, Com-
pleting preprocessing, Completing model training, Transforming input parame-
ters, Outputting results via the interface).
7. Business Rule (BR): formal instructions that regulate, limit, establish the
context and framework for the functioning of processes. Example of a business
rule: the format of images must be JPG, JPEG, PNG).
Thus, on the basis of the conceptual model (meta-model) of the image text
replacement system with style preservation shown in Fig. 1, we can reasonably
formalise the structural representation of the image text replacement system with
style preservation in the form of a component diagram (Fig. 2).
Formalisation of the structural representation of the conceptual model of the
system
Such a conceptual model of structural representation (Fig. 2) formalises the class
of systems for replacing text in an image with preserving the style in the form of
an ordered set of entities and relations between them. An important property of
such a conceptual model is the ability to implement the internal structure of the
system components on the basis of various engineering and mathematical tools
necessary for the implementation of a particular system without changing the
structure and interfaces of interaction between the components.
Here is a short list of the system’s interfaces and their functionality:
IDD (Interface Dataset Download) — interface for downloading an exter-
nal data set;
IIP (Interface Image Preprocessing) — interface for transferring the ini-
tially processed downloaded data set;
ISIP (Interface Segmented Image Processing) — interface for transferring
processed segmented images with annotations;
IMT (Interface Model Training) — interface for transferring the trained model;
IIITP (Interface Input Image and Text Processing) — an interface for
transmitting the result of processing images and text entered by the user.
Based on the model of structural representation, we formalise the model of
dynamic representation of the system, which shows the internal structure of com-
ponents and the algorithm of interaction between them (Figs. 3, 4).
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ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 66
Formalisation of the dynamic representation of the conceptual model of the
system
The formalisation of the dynamic representation is determined on the basis of a
set of processes specific to the class of Data Science systems, according to the
Data Science process defined by O’Neill and Schutt [2] and the international
standard CRISP-DM as interpreted by Foster and Fawcett [3] (Figs. 3, 4).
At this level, the organisation of activities can be refined to take into account
the specifics of the system and, as a result, decomposed into the following three
sub-stages:
1. Collection, analysis, and processing of training text data to be used in
model training according to the Data Science process model proposed by O’Neill
and Schutt [2] or the Data under standing and Data processing stages of the
CRISP-DM (CRoss Industry Standard Process for Data Mining) analysed by
Foster and Fawcett [3].
2. The actual construction (architecture development) and training of the
model is analogous to the Machine Learning Algorithms Statistical Models stage
[2] or the Modeling stage of the standard Data Mining process [3].
3. Determining metrics for evaluating the effectiveness of both trained
models and the system as a whole is analogous to the Report Findings stage [2] or
the Evaluation stage of the standard Data Mining process [3].
Fig. 2. Conceptual model of a system for replacing text in an image while preserving the
style. Component diagram in UML notation
A conceptual model and a system for replacing text in an image while preserving…
Системні дослідження та інформаційні технології, 2025, № 2 67
Thus, the Eriksson–Penker business profile is a system of classes and
relationships between them that are necessary and sufficient to represent and
develop a conceptual model of the system. And the corresponding set of Data
Science technologies are tools for implementing the components of the
conceptual model of the system.
An exhaustive list of business rules (specifications) for a business profile
regulates the functionality of the user interface of a particular system, for example:
BR1 — The user must enter new text and select a replacement image before
starting the process. If this data is not provided, the system does not start
processing and sends a request to fill in all fields.
BR2 — If the user uploads an image for the style, it must comply with the
established formats (JPEG, JPG, PNG) and be no larger than 10 MB.
BR3 — After the text replacement is complete, the user can preview the
result before uploading it.
BR4 — If an error occurs (incorrect image loading, segmentation errors,
insufficient data for training, etc.), the system should stop the process and send a
clear error message to the user indicating possible ways to fix it.
Implementation of the conceptual model for a specific version of the system
for replacing text in an image while preserving the style. Implementation of
the structural representation of the system
Since the conceptual model of the system is a system of classes and relations be-
tween them, necessary and sufficient for the functioning of the system (Fig. 1),
and the conceptual model of the system in the form of a diagram of components
has the form shown in Fig. 2, then the purpose and functionality of the system
components will be as follows:
Fig. 3. Conceptual model of a system for replacing text in an image while preserving the
style. The first level activity diagram in UML notation
P. Maslianko, M. Romanov
ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 68
1. Training data loading. This module is responsible for loading images
from the IMGUR5K dataset [9], checking their integrity using hashes, and load-
ing annotations for use in further processing steps.
2. Training data preprocessing. This module is responsible for segmenting
the uploaded images and saving them to the appropriate directories for training,
validation and testing. It is also possible to reduce the size of the training dataset
by random sampling, which is useful in situations of lack of computing power.
3. Model training. The main module of the system, which results in a trained
model for transforming the entered images and text. For training, pre-segmented
images are used with corresponding annotations that describe the text on the seg-
mented image.
Fig. 4. Conceptual model of a system for replacing text in an image while preserving the
style. A detailed process diagram based on a second-level activity diagram in UML notation
A conceptual model and a system for replacing text in an image while preserving…
Системні дослідження та інформаційні технології, 2025, № 2 69
4. Replacing text with images. A module that serves as a facade for interact-
ing with the trained model, where the trained model is loaded into memory, input
parameters are passed to the model, and the results are returned in the form of
generated images.
5. User interface. A module that is an interface (web page) where a user can
upload their image with a style and enter text to generate an image with text in the
style of the uploaded image.
Implementation of a dynamic system representation.The dynamic repre-
sentation of the system in the form of an activity diagram (Fig. 3) is a visualisa-
tion of the main cycles of the processes and their first-level interaction.
Fig. 4 shows a diagram of the processes of the conceptual model of the sys-
tem in the form of a diagram of interaction of components (see Fig. 2) and shows
their internal structure. Such a representation makes it possible to group the main
processes of the system by the relevant components. The process diagram is an
integral part of the conceptual model based on the Eriksson–Penker business pro-
file, as it combines both types of representations of the system, structural and dy-
namic, and formalises its activities.
Implementation of the Training Data Loading and Training Data Preproc-
essing components
Data download module. This module is responsible for loading images from the
IMGUR5K dataset [9], checking their integrity using hashes, and loading annota-
tions for use in further processing steps.
Uploading images: For each image, the module generates a URL, uses the
requests library to download it, and saves the file in a specific directory. After the
image is uploaded, its hash is checked to ensure authenticity.
Parallel uploading: To optimise the uploading process, the module uses the
ThreadPoolExecutor from the futures library, which allows you to upload several
images simultaneously, which significantly speeds up processing.
Loading annotations: After the images are successfully uploaded, the module
loads annotations for each image in JSON format, containing information about
the image, its hash, and the coordinates of text elements. The annotations are also
divided into three sets: training, validation, and test.
Training data processing module. This module is responsible for process-
ing the uploaded images, extracting text features, and preparing the dataset for
model training.
Processing of text elements: For each image, the module uses annotations to
find text features in the images. The crop_minAreaRect() function is used to
crop the image around the text, taking into account its coordinates and orienta-
tion. This function is based on the perspective transformation algorithm
cv2.getPerspectiveTransform() from the OpenCV library, which allows you to
accurately select text taking into account its slope.
Saving processed images: The cropped text elements are saved in PNG for-
mat, and a corresponding record is created in the JSON file with the text on the
image for each of them.
Reducing the data sample: If you specify the reduce option, the module ran-
domly selects a part of the images for processing, which allows you to work with
a smaller sample for testing or speeding up the work.
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Implementation of the Model Training component. Model training process:
1. Load the training dataset (segmented images with annotations).
2. Creating an initialised model of the initial architecture using available py-
thon libraries and frameworks / loading a pre-trained model — the latest version
of the saved model.
3. Training the model using the GAN algorithm on the training data set by
optimising the loss function.
4. Conducting validation to determine the optimal model architecture and
model hyperparameters on the validation dataset with the calculation of MSE,
SSIM, PSNR and FID metrics.
5. Final testing on the test data set.
6. Saving the current version of the model, selecting the most efficient ve
7. rsion for processing user requests.
Mathematical support of the model training component
In Fig. 5 we can see a high-level diagram of the model training process. This dia-
gram is based on the TextStyleBrush architecture [9]. It shows the main compo-
nents of the model, and we will describe them:
1. Content — an image of the text we want to see on the generated image.
This image is formed from the annotations to the images, namely, the text is taken
and transformed into an image using a file that describes the font (VerilySerif-
Mono) and functions from the PIL library. The size of all images is fixed — 192
by 64 pixels. This is done in order to have the same data types in all modules of
our model, which greatly simplifies implementation.
2. Style content — an image of the text that appears on the image with the
desired style.
3. Style image — an image that contains the desired style for the generated image.
4. Content Encoder — it is a pre-trained ResNet18 model that does not in-
clude average pooling layers and all subsequent ones, which is done to preserve
the spatial properties of the image.
5. Style Encoder — it is a pre-trained ResNet18 model that lacks fully-
connected layers.
Fig. 5. High-level diagram of the model training process
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Системні дослідження та інформаційні технології, 2025, № 2 71
6. Style mapping network — the key component of the StyleGAN model,
which transforms the style vector obtained by the Style Encoder into a vector of
parameters used to control the style on each layer of the generator using fully
connected layers. The network structure consists of the following layers: Pix-
elNorm (for normalizing the values of the vector obtained from the StyleEncoder
component), Linear (fully connected layer), and LeakyReLU (nonlinear activation
function).
7. StyleGAN generator — is the main component of the model that is re-
sponsible for generating images, taking as input an image with text and a vector
of parameters obtained using the Style mapping network.
The synthesizing network consists of several convolutional units that
gradually increase the image resolution starting from a small initial feature map.
Each block uses stylized layers, such as AdaIN [10], to control the style of the
image.
AdaIN (Adaptive Instance Normalization) — is a key component of
StyleGAN that is responsible for styling images based on the style derived from
the mapping network. AdaIN normalizes the feature activations for each feature
map separately, and then scales and shifts their values according to the parameters
obtained from the style vector.
Formally, the work of this layer can be represented as follows:
Let’s say we have: x — input feature tensor for the convolutional layer; w
— style vector obtained from the mapping network; )(x and )(x — mean and
standard deviation for any channel.
First, we calculate the affine transformation:
bwy A , by A
where A and A — weight matrices for multiplying the vector w, which pro-
vide different scaling depending on the style; b and b — displacement vectors
that shift the parameters that control the mean value (for y ) and standard devia-
tion (for y ) of features in the image space.
After the affine transformation, the style vector w creates the parameters y
and y , which are then passed to AdaIN. Each feature channel in the tensor is
normalized and modified according to these parameters, allowing each channel to
respond to the style w .
Next, the features are normalized, where the mean )(x and standard
deviation )(x are calculated for each channel, and then the value is normalized:
)(
)(
x
xx
x ij
ij
.
After normalization, each channel is scaled by y and shifted by y , which
allows you to change the style:
yxyxAdaIN
Thus, the affine transformation helps to transfer style parameters from the
style vector w to the feature level, where they determine the color, texture, and
other characteristics of the generated image. This allows for flexible control of
visual attributes through the latent vector w .
P. Maslianko, M. Romanov
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8. Prediction — an image generated by the generator that combines a style
from the Style image and text from the Content image.
9. Reconstruction — is an image generated by a generator that combines the
style from the Style image and the text from the Style content image; under ideal
conditions, the Reconstruction image should be identical to the Style image.
Next, we will describe additional components of the system that help calcu-
late the loss function for our model.
Let’s start with the discriminator (Fig. 6), as we can see, using the Discrimi-
nator component we calculate two loss functions — Discriminator adversarial
loss and Generator adversarial loss. To calculate these two functions, we use the
classical approach for GAN models, namely:
))0)), , ((()1), (((
2
1
imagestylecontentstyleimagestyletordiscrimina zzGDMSEzDMSEL ,
where MSE — mean squared error; D — discriminator; G — generator;
imagestylez and contentstylez — the results of the Style Encoder and Content En-
coder components, respectively.
The generator loss function is calculated as follows:
))1)), , ((( imagestylecontentstylegenerator zzGDMSEL .
To calculate the OCR loss function (Fig. 7), we use the trained TRBA (Text
Recognition by Attention) model, which is denoted as OCR Recognizer in the
diagram, Content label and Style content label are the text in the image Content
and Style content, respectively, which are available during training.
In order to calculate the OCR loss function, we follow the following steps:
Normalize the image size.
Encode the text labels Content label and Style content label.
Pass the Prediction and Reconstruction images to the model and get the
predicted text on these images in the encoded form.
Calculate the cross-entropy for the pairs (Prediction, Content label) and
(Reconstruction, Style content label).
Sum the obtained values and divide them by two.
Fig. 6. Diagram of the process of calculating the competitive loss function for the dis-
criminator and generator
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Системні дослідження та інформаційні технології, 2025, № 2 73
Formally, the process of calculating the OCR loss function can be
represented as follows:
) ),(((
2
1
labelContentPredictionTRBACELOCR
) ),(() labelcontentStyletionReconstrucTRBACE ,
where CE — cross entropy; TRBA — pretrained model TRBA.
To calculate the Reconstruction of the loss function (Fig. 8), we need to
calculate the difference between the Style image and the Reconstruction image.
Formally, this is written as follows:
), (1 tionReconstrucimageStyleLL tionreconstruc ,
where 1L — is the 1L loss function or mean absolute error.
Fig. 7. Diagram of the OCR loss function calculation process
Fig. 8. Diagram of the process of calculating the Reconstruction loss function
P. Maslianko, M. Romanov
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To calculate the Cycle of the loss function (Fig. 9), we first need to get a
Reconstruction image, and then pass it to the model input and get another image.
The idea is that we should lose as little data as possible during the transformations
driven by the model. Once we have two images, we calculate the average absolute
error between them:
To calculate the Typeface loss function (Fig. 10), we use the pre-trained
VGG16 model, in which the last classification layer is removed, which means that
the model will not perform classification, but will simply return a feature vector.
After that, we pass two images to the model input — Style image and
Reconstruction and calculate the L1 loss function between them, formally written
as follows:
Fig. 9. Diagram of the process of calculating the Cycle of the loss function
Fig. 10. Diagram of the Typeface loss function calculation process
A conceptual model and a system for replacing text in an image while preserving…
Системні дослідження та інформаційні технології, 2025, № 2 75
))(16), (16(1 tionReconstrucVGGimageStyleVGGLLtypeface .
To calculate the Perceptual and Texture loss functions (Fig. 11), we use the
trained VGG16 model. For each block of VGG16, the difference between the ac-
tivations of the corresponding layers of the input and target images is calculated,
and the difference is measured using L1 loss, thus we obtain the Perceptual loss
function:
wh
YXL
Lperceptual
), (1
,
where X and Y — are activations at the i-th layer for the input and target images,
respectively; h and w — height and width of the corresponding activations.
Texture losses are calculated in a similar way, but additionally, Gram matri-
ces are calculated (The element ijG represents the mutual correlation between the
i-th and j-th channels. It calculates how similar these two channels are on average,
i.e. how much the pixel value variations of one channel are related to the varia-
tions of the other. The gram matrix provides an idea of how different parts of the
filters “cooperate” with each other to reflect the style or texture of the image):
wh
YGXGL
Ltexture
))(),((1
,
where )(XG and )(YG — are Gram matrices, which are defined as
T )( XXXG .
The final loss function of the generator is as follows:
cycletionreconstrucOCRgeneratorfinal LLLLL 0.20.207.006.0
textureperceptualtypeface LLL 0.70.250.0 .
The coefficients were selected empirically.
Fig. 11. Diagram of the process of calculating Perceptual and Texture loss functions
P. Maslianko, M. Romanov
ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 76
Verification and validation of the system engineering method of the system
The fourth stage involves verification and validation of the developed system to
ensure that all technical specifications and stakeholder requirements are met. First
of all, the model used to implement the Model Training component should be
taken into account. It can be one of the following types of neural network archi-
tectures (this list is not exhaustive):
convolutional neural networks;
recurrent neural networks;
generative-adversarial neural networks.
Next, you can analyze the functionality, adequacy, and performance of the
developed system based on the selected benchmarking metric.
So, based on the definition of the concept of “method” [4], we can formulate
the definition of the System Engineering Method of the conceptual model and the
system of text replacement in images with style preservation: it is an ordered set
of classes of tasks, processes, resources, business rules and relationships between
them to produce a system based on the system engineering methodology, the
Eriksson–Penker business profile and Data Science technologies.
Defining criteria for evaluating system performance
When designing a specific text-to-picture image replacement system, it is impor-
tant to use appropriate metrics to evaluate the quality of image generation. The
goal of such metrics is to measure how well the new images match the original in
terms of visual similarity, stylistic authenticity, and detail accuracy. The metrics
used in this paper are: mean square error (MSE), structural similarity (SSIM),
peak signal-to-noise ratio (PSNR), and Frechet distance between distributions
(FID). Each of them assesses different aspects of system quality and is effective
for evaluating the performance of generative models.
1. Mean squared error (MSE)
Mean Squared Error (MSE) — is a metric that evaluates the difference
between the pixel values in the original image and the generated image. It is
calculated as the arithmetic mean of the square deviations between the
corresponding pixels of the two images. The formula for calculating MSE looks
like this:
2
genorig
1
))()((
1
iIiI
N
MSE
N
i
,
where N — number of pixels in the image; )(orig iI and )(gen iI — pixel values
of the original and generated images, respectively.
Advantages of MSE:
Easy to implement and interpret;
Well suited for evaluating pixel-by-pixel rendering accuracy.
Why it’s useful: MSE allows you to measure the exact difference between
the original and the generated image, which is important for assessing the quality
of preservation of text elements and image details after text replacement.
2. Structural similarity (SSIM)
Structural Similarity Index Measure (SSIM) — is a metric that evaluates the
similarity between two images in terms of their structure, brightness, and contrast.
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Системні дослідження та інформаційні технології, 2025, № 2 77
SSIM tries to model human perception, which makes it more sensitive to changes
in image structure [11]. SSIM calculation formula:
)()(
)2()2(
),(
2
2
gen
2
orig1
2
gen
2
orig
2gen-orig1genorig
genorig
CC
CC
IISSIM
,
where orig and gen — average pixel values of the original and generated im-
ages; orig and gen — covariance between the original and generated image;
1C and 2C — small constants to stabilize calculations.
Advantages of:
Good at showing structural changes in an image that are difficult to detect
with MSE.
Simulates visual perception, making it more suitable for assessing image
quality from the perspective of the human eye.
Why it’s useful: SSIM helps to assess how well the basic structures and tex-
tures of the original image are preserved after text replacement, which is impor-
tant for maintaining the style and authenticity of visual content.
3. Peak Signal-to-Noise Ratio (PSNR)
Peak Signal-to-Noise Ratio (PSNR) — is a metric used to evaluate the
differences between an original and a generated image by measuring the level of
noise or distortion. PSNR is calculated based on MSE and is expressed in decibels
(dB) [12]:
2
10
MAX
PSNR 10 log
MSE
,
where MAX — is the maximum possible pixel value in the image (for example,
255 for 8-bit images), and MSE is the root mean square error.
Advantages of PSNR:
Sensitive to small changes in brightness and colour.
A high PSNR value indicates that the image is close to the original.
Why it’s useful: PSNR allows you to evaluate how well the system preserves
brightness and contrast after text replacement, which is important for ensuring the
visual quality of the image after editing.
4. Fréchet Inception Distance (FID)
Fréchet Inception Distance (FID) — is a metric used to evaluate the quality
of generated images based on the similarity of distributions between real and
generated images. FID measures the distance between the Gaussian distributions
of features from images that are extracted using a pre-trained neural network
(usually InceptionV3) [13]. FID calculation formula:
))ΣΣ(2Σ(ΣTrμμFID 2/1
genoriggenorig
2
genorig ,
where orig and gen — average feature values for original and generated im-
ages, origΣ and genΣ — covariation of these features.
Advantages of:
Evaluates not only pixel similarity, but also deeper features of images, mak-
ing it more flexible for evaluating high-level stylistic characteristics.
P. Maslianko, M. Romanov
ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 78
Metric sensitivity to small changes that can be important for generating
visually realistic images.
Why it’s useful: FID is one of the main metrics for evaluating the quality of
generative models such as StyleGAN. It allows you to evaluate how well the
system is able to generate realistic images while preserving the visual and stylistic
characteristics of the text and overall composition.
Conclusion. The use of MSE, SSIM, PSNR, and FID metrics provides a
compreh ensive approach to evaluating the performance of a style-preserving
text-to-picture system. MSE and PSNR evaluate the pixel-by-pixel reproduction
accuracy, SSIM takes into account structural similarity, and FID allows you to
determine how realistic the generated images look. Together, these metrics help to
comprehensively evaluate the quality of the system and its ability to accurately
reproduce the stylistic and visual features of images after text replacement.
Verification and validation of the system engineering method
The fourth stage involves verification and validation of the conceptual model and
the developed version of the image text replacement system with style preserva-
tion. The model was built on the basis of the TextStyleBrush model architecture
and trained for 50 epochs on a dataset consisting of 20 thousand segmented im-
ages. The system was also verified and validated, and the results are shown in the
following Table 2.
T a b l e 2 . Comparative analysis of the use of metrics to assess the quality of
the developed system
Metric Test set 1 Test set 2 Test set 3 Average
MSE 0.0196 0.0229 0.0288 0.0238
SSIM 0.5071 0.5489 0.4460 0.5007
PSNR 15.3077 15.8087 15.4113 15.5092
FID 1.3876 0.7256 1.1510 1.0881
Interpreting the results of metrics such as MSE, SSIM, PSNR, and FID can
give you an idea of the quality of the generated images compared to the original
ones. Here is a breakdown of what each metric typically shows:
1. Mean squared error (MSE)
MSE quantifies the root mean square difference between the pixel values of
two images. Lower values indicate better similarity. Although there is no absolute
scale, an MSE value close to zero indicates high similarity, while higher values
indicate greater divergence.
2. Structural similarity index (SSIM)
The SSIM ranges from -1 to 1, where 1 indicates complete similarity. An
SSIM value of 0.5 indicates moderate structural similarity between images. In
general, values above 0.7 are considered acceptable, and values above 0.9 are
considered excellent.
3. Peak signal-to-noise ratio (PSNR)
PSNR is a logarithmic measure that compares the maximum possible signal
power with the power of distorting noise. In image processing, a PSNR of 20–30
dB is generally considered acceptable, while a value of more than 30 dB is good
and more than 40 dB is excellent. A PSNR value of 15 dB indicates poor quality,
often indicating that the generated images are very different from the originals.
4. Fréchet Inception Distance (FID)
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Системні дослідження та інформаційні технології, 2025, № 2 79
FID compares the distribution of generated images to real images. Lower
FID scores indicate better quality and diversity of the generated images. Scores
below 10 are generally considered good, while scores above 50 indicate poor
quality. A FID score of 1.0881 indicates that your GAN is generating high quality
images that are very similar to the real dataset.
Overall score:
MSE: Low, which is good.
SSIM: Moderate; can be improved.
PSNR: Low; indicates noticeable differences in image quality.
FID: Excellent; indicates good GAN performance.
Conclusion. MSE, SSIM, and PSNR metrics are used in almost every work
related to generative networks, they are simple and quick to calculate, but such
simplicity is usually not suitable for evaluating such systems, it is more done to be
able to compare results with older works. It is better to focus on the results of the
FID metric, which more accurately reflects the result for our particular problem.
CONCLUSIONS
1. A method of system engineering of a conceptual model and a system for re-
placing text in images with preservation of style is proposed, based on the modi-
fied Eriksson–Penker business profile [5–8] of the system’s representation at the
meta-level, as well as international standards of DataScience [2] and DataMining
[3] processes, which is the basis for algorithmizing the development of specific
system components. The effectiveness of the method is investigated on the exam-
ple of developing a system for automatic text replacement in an image while pre-
serving the style.
2. The proposed method of system engineering is aimed at developing
specialized systems designed to replace text on design layouts and various
products of companies and brands that need to adapt their visual materials for
different markets and languages for promoting goods and services.
3. The use of the system engineering method significantly speeds up and
streamlines the implementation of a particular system and reduces the cost of its
development, verification and validation.
4. Prospects for further research are aimed at applying the system
engineering method to implement a system based on other mathematical models,
forming performance evaluation metrics and scientifically sound methods for
verifying and validating the method.
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Received 25.11.2024
INFORMATION ON THE ARTICLE
Pavlo P. Maslianko, ORCID: 0000-0003-4001-7811, National Technical University of
Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine, e-mail: masliankop@gmail.com
Mykola D. Romanov, National Technical University of Ukraine “Igor Sikorsky Kyiv
Polytechnic Institute”, Ukraine, e-mail: kolya.romanov8@gmail.com
КОНЦЕПТУАЛЬНА МОДЕЛЬ ТА СИСТЕМА ДЛЯ ЗАМІНИ ТЕКСТУ НА
ЗОБРАЖЕННІ ЗІ ЗБЕРЕЖЕННЯМ СТИЛЮ / П.П. Маслянко, М.Д. Романов
Анотація. Заміна тексту на зображенні, зокрема, зі збереженням його стилю, є
складним завданням, яке потребує вирішення низки наукових задач та нових
технічних рішень. Однією з основних проблем є збереження автентичності та
гармонійності зображення після внесення змін. Мета дослідження — розроб-
лення концептуальної моделі та системи заміни тексту на зображеннях зі збе-
реженням його стилю на основі методології системної інженерії та бізнес-
профілю Еріксона–Пенкера, забезпечуючи природну інтеграцію нових тексто-
вих елементів у контекст зображення. Методика реалізації — методологія сис-
темної інженерії і бізнес-профіль Еріксона–Пенкера для формалізації впоряд-
кованого процесу розроблення системи для заміни тексту на зображенні зі
збереженням стилю. Результати дослідження — метод розроблення системи на
основі застосування технік системної інженерії, який складається з чотирьох
основних етапів. На першому етапі структуру системи моделюють як бізнес-
профіль Еріксона–Пенкера, на другому — визначають множину процесів, ха-
рактерну для класу систем Data Science та міжнародного стандарту CRISP-DM,
моделюють структурне і динамічне представлення концептуальної моделі сис-
теми та інтерфейси взаємодії компонентів, на третьому етапі виконують імп-
лементацію конкретної версії системи, а на четвертому — верифікація та валі-
дація системи. Запропоновано метод системної інженерії концептуальної
моделі та системи заміни тексту на зображеннях зі збереженням його стилю,
що ґрунтується на модифікованому бізнес-профілі Еріксона–Пенкера подання
системи на метарівні, а також міжнародних стандартів процесів Data Science та
Data Mining.
Ключові слова: метод системної інженерії, бізнес профіль Еріксона–Пенкера,
концептуальна модель, система для заміни тексту на зображенні зі збережен-
ням стилю.
|
| id | journaliasakpiua-article-335821 |
| institution | System research and information technologies |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2025-09-17T09:26:03Z |
| publishDate | 2025 |
| publisher | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
| record_format | ojs |
| resource_txt_mv | journaliasakpiua/d6/1c1ab8e4af3bd066d118a4936c2b03d6.pdf |
| spelling | journaliasakpiua-article-3358212025-07-25T15:56:08Z A conceptual model and a system for replacing text in an image while preserving the style Концептуальна модель та система для заміни тексту на зображенні зі збереженням стилю Maslianko, Pavlo Romanov, Mykola метод системної інженерії бізнес профіль Еріксона–Пенкера концептуальна модель система для заміни тексту на зображенні зі збереженням стилю systems engineering method Eriksson-Penker business profile conceptual model system for text replacement in images with style preservation Text replacement in images, particularly while preserving its style, is a complex task that requires solving a range of scientific challenges and developing new technical solutions. One of the main issues is maintaining the authenticity and harmony of the image after modifications. The Research Objective is the development of a conceptual model and a system for text replacement in images with style preservation based on systems engineering methodology and the Eriksson-Penker business profile, ensuring the natural integration of new text elements into the image’s context. Implementation Methodology – the systems engineering methodology and the Eriksson-Penker business profile are used to formalize the structured process of developing a system for text replacement in images with style preservation. Research Results – a method for developing the system based on systems engineering techniques was proposed, consisting of four main stages. In the first stage, the system structure is modeled as an Eriksson-Penker business profile. In the second stage, a set of processes is defined that are characteristic of the Data Science system class and the CRISP-DM international standard. Also, the structural and dynamic representations of the conceptual model, as well as the component interaction interfaces, are modeled. The third stage involves implementing a specific version of the system, while the fourth stage focuses on system verification and validation. A systems engineering method for the conceptual model and system for text replacement in images with style preservation has been proposed. It is based on a modified Eriksson-Penker business profile for metalevel system representation and international standards for Data Science and Data Mining processes. Заміна тексту на зображенні, зокрема, зі збереженням його стилю, є складним завданням, яке потребує вирішення низки наукових задач та нових технічних рішень. Однією з основних проблем є збереження автентичності та гармонійності зображення після внесення змін. Мета дослідження — розроблення концептуальної моделі та системи заміни тексту на зображеннях зі збереженням його стилю на основі методології системної інженерії та бізнес-профілю Еріксона–Пенкера, забезпечуючи природну інтеграцію нових текстових елементів у контекст зображення. Методика реалізації — методологія системної інженерії і бізнес-профіль Еріксона–Пенкера для формалізації впорядкованого процесу розроблення системи для заміни тексту на зображенні зі збереженням стилю. Результати дослідження — метод розроблення системи на основі застосування технік системної інженерії, який складається з чотирьох основних етапів. На першому етапі структуру системи моделюють як бізнес-профіль Еріксона–Пенкера, на другому — визначають множину процесів, характерну для класу систем Data Science та міжнародного стандарту CRISP-DM, моделюють структурне і динамічне представлення концептуальної моделі системи та інтерфейси взаємодії компонентів, на третьому етапі виконують імплементацію конкретної версії системи, а на четвертому — верифікація та валідація системи. Запропоновано метод системної інженерії концептуальної моделі та системи заміни тексту на зображеннях зі збереженням його стилю, що ґрунтується на модифікованому бізнес-профілі Еріксона–Пенкера подання системи на метарівні, а також міжнародних стандартів процесів Data Science та Data Mining. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2025-06-28 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/335821 10.20535/SRIT.2308-8893.2025.2.04 System research and information technologies; No. 2 (2025); 61-80 Системные исследования и информационные технологии; № 2 (2025); 61-80 Системні дослідження та інформаційні технології; № 2 (2025); 61-80 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/335821/324682 |
| spellingShingle | метод системної інженерії бізнес профіль Еріксона–Пенкера концептуальна модель система для заміни тексту на зображенні зі збереженням стилю Maslianko, Pavlo Romanov, Mykola Концептуальна модель та система для заміни тексту на зображенні зі збереженням стилю |
| title | Концептуальна модель та система для заміни тексту на зображенні зі збереженням стилю |
| title_alt | A conceptual model and a system for replacing text in an image while preserving the style |
| title_full | Концептуальна модель та система для заміни тексту на зображенні зі збереженням стилю |
| title_fullStr | Концептуальна модель та система для заміни тексту на зображенні зі збереженням стилю |
| title_full_unstemmed | Концептуальна модель та система для заміни тексту на зображенні зі збереженням стилю |
| title_short | Концептуальна модель та система для заміни тексту на зображенні зі збереженням стилю |
| title_sort | концептуальна модель та система для заміни тексту на зображенні зі збереженням стилю |
| topic | метод системної інженерії бізнес профіль Еріксона–Пенкера концептуальна модель система для заміни тексту на зображенні зі збереженням стилю |
| topic_facet | метод системної інженерії бізнес профіль Еріксона–Пенкера концептуальна модель система для заміни тексту на зображенні зі збереженням стилю systems engineering method Eriksson-Penker business profile conceptual model system for text replacement in images with style preservation |
| url | https://journal.iasa.kpi.ua/article/view/335821 |
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