Log in

Relevant bibliographies by topics / Handwritten text Image generation / Journal articles

To see the other types of publications on this topic, follow the link: Handwritten text Image generation.

Journal articles on the topic 'Handwritten text Image generation'

Author: Grafiati

Published: 2 June 2025

Last updated: 19 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Handwritten text Image generation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Dubey, Parul, Manjushree Nayak, Hitesh Gehani, Ashwini Kukade, Vinay Keswani, and Pushkar Dubey. "Enhancing realism in handwritten text images with generative adversarial networks." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2370–79. https://doi.org/10.11591/eei.v14i3.9190.

Full text

Abstract:

Image synthesis is particularly important for applications that want to create realistic handwritten documents, which is why handwritten text generation is a critical area within its domain. Even with today's highly advanced technology, generating diverse and accurate representations of human handwriting is still a tough problem because of the variability in style. In this study, we tackle the problem of instability during the training phase of generative adversarial networks (GANs) for generating handwritten text images. Using the MNIST dataset, which includes 60,000 training and 10,000 test

APA, Harvard, Vancouver, ISO, and other styles

2

Surolia, Akshat. "Ooze - Handwritten Text Generator." GLS KALP: Journal of Multidisciplinary Studies 1, no. 4 (2024): 35–49. http://dx.doi.org/10.69974/glskalp.01.04.64.

Full text

Abstract:

In this paper we show how Generative Adversarial Network (Goodfellow et al., 2014), more specifically Cycle-GAN(Zhu et al., 2017), can be used for Human like handwriting generation with output size up to a text line. The methodology used in Cycle-GAN is to establish translation between two different domains (e.g., connection between image of horse and zebra), extending this methodology to establishment of translation between machine printed text and handwritten text is mentioned in this paper. The neural network used in this paper is trained for dataset created by the author and can be trained

APA, Harvard, Vancouver, ISO, and other styles

3

Bogatenkova, Anastasiya Olegovna, Oksana Vladimirovna Belyaeva, and Andrey Igorevich Perminov. "Generation of Images with Handwritten Text in Russian." Proceedings of the Institute for System Programming of the RAS 35, no. 2 (2023): 19–34. http://dx.doi.org/10.15514/ispras-2023-35(2)-2.

Full text

Abstract:

Automatic handwriting recognition is an important component in the process of electronic documents analysis, but its solution is still far from ideal. One of the main reasons for the complexity of Russian handwriting recognition is the insufficient amount of data used to train recognition models. Moreover, for the Russian language the problem is more acute and is exacerbated by a large variety of complex handwriting. This paper explores the impact of various methods of generating additional training datasets on the quality of recognition models: the method based on handwritten fonts, the Stack

APA, Harvard, Vancouver, ISO, and other styles

4

Bogatenkova, A. O., O. V. Belyaeva, and A. I. Perminov. "Generation of Images with Handwritten Text in Russian." Programming and Computer Software 50, no. 7 (2024): 483–92. https://doi.org/10.1134/s036176882470021x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Li, Rankang, Shanxiong Chen, Fujia Zhao, and Xiaogang Qiu. "Text Detection Model for Historical Documents Using CNN and MSER." Journal of Database Management 34, no. 1 (2023): 1–23. http://dx.doi.org/10.4018/jdm.322086.

Full text

Abstract:

This article introduces a text detection model for historical documents images. The handwritten characters in historical documents are always difficult to detect because they contain fuzzy or missing ink, or weathering features and stains; these features will seriously affect the detection accuracy. In order to reduce the influence mentioned above, an effective ATD model is proposed to detect the textbox of characters in historical documents image, and ATD model includes a CNN-based text-box generation network and an NMS-based MSER text-box generation model. As a post-processing method, a text

APA, Harvard, Vancouver, ISO, and other styles

6

Li, Minghao, Tengchao Lv, Jingye Chen, et al. "TrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13094–102. http://dx.doi.org/10.1609/aaai.v37i11.26538.

Full text

Abstract:

Text recognition is a long-standing research problem for document digitalization. Existing approaches are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effec

APA, Harvard, Vancouver, ISO, and other styles

7

Zhao, Fuchang. "Research on Handwriting Text Generation Algorithm Based on Generative Adversarial Network." Academic Journal of Science and Technology 9, no. 3 (2024): 194–97. http://dx.doi.org/10.54097/kt3yem44.

Full text

Abstract:

The main task of this paper is to study the handwriting text generation method based on deep learning. Through understanding the development status of the research, It can be found that the current research on the generation of different handwriting styles still has some obvious defects, such as the need for manual intervention in character segmentation, failure to capture the global handwriting style, style collapse, failure to generate arbitrary length characters, and text. Finally, this paper proposes a handwritten text generation algorithm combining advantages of convolutional network and

APA, Harvard, Vancouver, ISO, and other styles

8

Zhang, Likun, Xiaoyan Li, Yi Tang, Fangbin Song, Tian Xia, and Wei Wang. "Contemporary Advertising Text Art Design and Effect Evaluation by IoT Deep Learning under the Smart City." Security and Communication Networks 2022 (July 22, 2022): 1–14. http://dx.doi.org/10.1155/2022/5161398.

Full text

Abstract:

This work intends to solve the problem that the current artistic typeface generation methods rely too much on manual intervention, lack novelty, and the single font local feature and the global feature extraction method cannot fully describe the font features. Firstly, it proposes a handwritten word recognition model based on generalized search trees (GIST) and the pyramid histogram of oriented gradient (PHOG). The local features and global features of the font are fused. Secondly, a model of automatic artistic typeface generation based on generative adversarial networks (GAN) is constructed,

APA, Harvard, Vancouver, ISO, and other styles

9

Dinges, Laslo, Ayoub Al-Hamadi, Moftah Elzobi, Sherif El-etriby, and Ahmed Ghoneim. "ASM Based Synthesis of Handwritten Arabic Text Pages." Scientific World Journal 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/323575.

Full text

Abstract:

Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system th

APA, Harvard, Vancouver, ISO, and other styles

10

Han, Sangkwon, Seungbin Ji, and Jongtae Rhee. "Diffusion-Denoising Process with Gated U-Net for High-Quality Document Binarization." Applied Sciences 13, no. 20 (2023): 11141. http://dx.doi.org/10.3390/app132011141.

Full text

Abstract:

The binarization of degraded documents represents a crucial preprocessing task for various document analyses, including optical character recognition and historical document analysis. Various convolutional neural network models and generative models have been used for document binarization. However, these models often struggle to deliver generalized performance on noise types the model has not encountered during training and may have difficulty extracting intricate text strokes. We herein propose a novel approach to address these challenges by introducing the use of the latent diffusion model,

APA, Harvard, Vancouver, ISO, and other styles

11

Jeganathan, Balachandar. "Exploring the Power of Generative Adversarial Networks (GANs) for Image Generation: A Case Study on the MNIST Dataset." International Journal of Advances in Engineering and Management 7, no. 1 (2025): 21–46. https://doi.org/10.35629/5252-07012146.

Full text

Abstract:

This paper provides a comprehensive examination of Generative Adversarial Networks (GANs), a groundbreaking deep learning architecture that has transformed the field of image generation. GANs consist of two neural networks—the generator and the discriminator—that engage in a competitive process to produce highly realistic images by learning patterns from existing data. This study highlights several key applications of GANs, including text-to-image generation, superresolution, neural style transfer, andimage completion. These applications have significant implications for various industries, su

APA, Harvard, Vancouver, ISO, and other styles

12

Vural, Nazmi Ekin. "Design of an iOS Mobile Application for the Automated Evaluation of Open-Ended Exams via Artificial Intelligence and Image Processing." Sosyal Bilimler ve Eğitim Dergisi 8, no. 1 (2025): 1–35. https://doi.org/10.53047/josse.1691312.

Full text

Abstract:

Evaluating open-ended exams presents significant challenges in terms of time management and consistency in educational processes. This study aims to develop an iOS-based mobile application, “Exam Reader” to streamline the evaluation of handwritten open-ended exam responses by integrating visual recognition and language analysis tools, enabling educators to deliver timely and fair assessments. Developed using the Swift programming language, the application relies on two core technologies. First, handwritten student responses are converted into digital text using Optical Character Recognition (O

APA, Harvard, Vancouver, ISO, and other styles

13

Dli, Maxim I., Andrey Yu Puchkov, Boris V. Okunev, and Igor I. Tishchenko. "Algorithm for steganographic information protection in video files based on a diffusion-probabilistic model with noise reduction." Journal Of Applied Informatics 19, no. 3 (2024): 125–43. http://dx.doi.org/10.37791/2687-0649-2024-19-3-125-143.

Full text

Abstract:

The results of a study are presented, the purpose of which was to develop a steganography algorithm for hiding text messages in video files. The algorithm is based on the use of a diffusion-probability model with noise reduction, which is implemented by a deep artificial neural network. The algorithm consists of two parts – for the parties sending and receiving the message. On the transmitting side, the following is carried out: synthesis of handwritten images of symbols (signatures) of the line of the hidden message, alignment of their frequency; applying direct diffusion to signatures, resul

APA, Harvard, Vancouver, ISO, and other styles

14

Wang, S. H., S. Q. Lyu, M. L. Hou, Z. H. Gao, and M. Huang. "SURFACE HANDWRITING ENHANCEMENT OF ARTIFACTS BASED ON MANIFOLD LEARNING AND MIXED PIXEL DECOMPOSITION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 917–22. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-917-2022.

Full text

Abstract:

Abstract. Written information on the surface of cultural relics can record important historical events. Due to the influence of natural and human factors, the surface of cultural relics fades and the words are difficult to identify. Take advantage of the hyperspectral data image and spectral unity and wide spectral range, a cultural relics surface handwriting enhancement method based on manifold learning and mixed pixel decomposition was proposed. First, the minimum noise fraction (MNF) transformation was carried out on the hyperspectral image, and then the top 10 bands were selected for inver

APA, Harvard, Vancouver, ISO, and other styles

15

Joshi, Kalpesh. "Handwritten Text Recognition from Image." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1528–30. http://dx.doi.org/10.22214/ijraset.2023.53364.

Full text

Abstract:

Abstract: A computer vision program called Handwritten Text Recognition (HTR) attempts to recognize and translate handwritten text from scanned or photographed images. In this project, we suggest implementing an HTR system using Tesseract and OpenCV. English, Chinese, and Arabic are all supported by the popular open-source optical character recognition (OCR) engine known as Tesseract. It is employed to find and identify printed text within photographs. On the other hand, OpenCV is a well-liked computer vision library that offers several tools for processing and analyzing images. The pre-proces

APA, Harvard, Vancouver, ISO, and other styles

16

Shonenkov, A. V., D. K. Karachev, M. Y. Novopoltsev, M. S. Potanin, D. V. Dimitrov, and A. V. Chertok. "Handwritten text generation and strikethrough characters augmentation." Computer Optics 46, no. 3 (2022): 455–64. http://dx.doi.org/10.18287/2412-6179-co-1049.

Full text

Abstract:

We introduce two data augmentation techniques, which, used with a Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate and Character Error Rate beyond best-reported results on handwriting text recognition tasks. We apply a novel augmentation that simulates strikethrough text (HandWritten Blots) and a handwritten text generation method based on printed text (StackMix), which proved to be very effective in handwriting text recognition tasks. StackMix uses weakly-supervised framework to get character boundaries. Because these data augmentation techniques are independent of the network

APA, Harvard, Vancouver, ISO, and other styles

17

Mr., B. Ravinder Reddy, Nandini J., and Sowmya |. Y. Sathwik P. "Handwritten Text Recognition and Digital Text Conversion." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1826–27. https://doi.org/10.31142/ijtsrd23508.

Full text

Abstract:

Sometimes it is extremely difficult to secure handwritten documents in the real world. While doing so, we may encounter many problems such as misplacing the documents, unavailability of access from anywhere, physical damage, etc. So, to keep the information secure, we convert that information into digital format to address all the above mentioned problems. The main aim of our application is to recognize hand written text and display it in digital text format. Image processing is very significant process for data analysis these days. In image processing, the visible text from the real world as

APA, Harvard, Vancouver, ISO, and other styles

18

KATASEV, A. S., D. V. KATASEVA, YU N. SMIRNOV, and M. S. LITINSKY. "CONVOLUTIONAL NEURAL NETWORK MODEL FOR HANDWRITTEN MATHEMATICAL EXPRESSIONS RECOGNITION." Herald of Technological University 27, no. 6 (2024): 123–27. http://dx.doi.org/10.55421/1998-7072_2024_27_6_123.

Full text

Abstract:

This article is devoted to solving the problem of developing a convolutional neural network model for recognizing handwritten mathematical expressions consisting of Arabic numerals and basic symbols of mathematical operations. Its solution required the formation of training and test samples, development of the structure of the neural network and its training, selection of a method for segmenting the image into individual characters, as well as testing and evaluating the results of the convolutional neural network model. In the work, image processing and classification was carried out using a m

APA, Harvard, Vancouver, ISO, and other styles

19

Et.al, Siddharth Salar. "Automate Identification and Recognition of Handwritten Text from an Image." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 3800–3808. http://dx.doi.org/10.17762/turcomat.v12i3.1666.

Full text

Abstract:

Handwritten text acknowledgment is yet an open examination issue in the area of Optical Character Recognition (OCR). This paper proposes a productive methodology towards the advancement of handwritten text acknowledgment frameworks. The primary goal of this task is to create AI calculation to empower element and information extraction from records with manually written explanations, with an, expect to distinguish transcribed words on a picture. The main aim of this project is to extract text, this text can be handwritten text or it can machine printed text and convert it into computer un

APA, Harvard, Vancouver, ISO, and other styles

20

G R, Hemanth, Jayasree M, Keerthi Venii S, Akshaya P, and Saranya R. "CNN-RNN BASED HANDWRITTEN TEXT RECOGNITION." ICTACT Journal on Soft Computing 12, no. 1 (2021): 2457–63. http://dx.doi.org/10.21917/ijsc.2021.0351.

Full text

Abstract:

At present most of the scripts are handwritten due to the ease of using a pen tip in place of a keyboard, hence errors are common due to illegibility of the human handwriting. To avoid this problem handwriting recognition is essential. Offline handwritten Text recognition (OHTR) has become one of the major areas of research in recent times because of the need to eliminate errors due to misinterpretation of handwritten text and the need for automation to improve efficiency. The application of this system can be seen in fields like handwritten application interpretations, postal address recognit

APA, Harvard, Vancouver, ISO, and other styles

21

Rosalina, Rosalina, Johanes Parlindungan Hutagalung, and Genta Sahuri. "Hiragana Handwriting Recognition Using Deep Neural Network Search." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 01 (2020): 161. http://dx.doi.org/10.3991/ijim.v14i01.11593.

Full text

Abstract:

<span id="orcid-id" class="orcid-id-https">These days there is a huge demand in “storing the information available in paper documents into a computer storage disk”. Digitizing manual filled forms lead to handwriting recognition, a process of translating handwriting into machine editable text. The main objective of this research is to to create an Android application able to recognize and predict the output of handwritten characters by training a neural network model. This research will implement deep neural network in recognizing handwritten text recognition especially to recognize digit

APA, Harvard, Vancouver, ISO, and other styles

22

Bhandari, Basant Babu, Aakash Raj Dhakal, Laxman Maharjan, and Asmin Karki. "Nepali Handwritten Letter Generation using GAN." Journal of Science and Engineering 9 (December 31, 2021): 49–55. http://dx.doi.org/10.3126/jsce.v9i9.46308.

Full text

Abstract:

The generative adversarial networks seem to work very effectively for training generative deep neural networks. The aim is to generate Nepali Handwritten letters using adversarial training in raster image format. Deep Convolutional generative network is used to generate Nepali handwritten letters. Proposed generative adversarial model that works on Devanagari 36 classes, each having 10,000 images, generates the Nepali Handwritten Letters that are similar to the real-life data-set of total size 360,000 images. The generated letters are obtained by simultaneously training the generator and discr

APA, Harvard, Vancouver, ISO, and other styles

23

Ritonga, Mahyudin, Manoj L. Bangare, Pushpa Manoj Bangare, et al. "Optimized convolutional neural network deep learning for Arabian handwritten text recognition." Bulletin of Electrical Engineering and Informatics 14, no. 2 (2025): 1497–506. https://doi.org/10.11591/eei.v14i2.7696.

Full text

Abstract:

In general, the term handwritten character recognition (HCR) refers to the process of recognizing handwritten characters in any form, whereas handwritten text recognition (HTR) refers to the process of reading scanned document images that include text lines and converting those text lines into editable text. The identification of recurring structures and configurations in data is the primary focus of the field of machine learning known as pattern recognition. Optical character recognition, often known as OCR, is a challenging issue to solve when it comes to the field of pattern recognition. Th

APA, Harvard, Vancouver, ISO, and other styles

24

Chakravarthy, ASN, Penmetsa V. Krishna Raja, and Avadhani. "Handwritten Text Image Authentication Using Back Propagation." International Journal of Network Security & Its Applications 3, no. 5 (2011): 121–30. http://dx.doi.org/10.5121/ijnsa.2011.3510.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

R P, Roshan. "An Overview of Handwritten Text Recognition Using Generative AI: Cutting-Edge Approaches, Key Challenges, and Future Directions." International Journal for Research in Applied Science and Engineering Technology 12, no. 9 (2024): 1097–101. http://dx.doi.org/10.22214/ijraset.2024.64315.

Full text

Abstract:

Recognizing handwritten text involves several stages, including data collection, data preparation, feature extraction, and categorization. It is essential for tasks like document processing, robotic automation, and historical document analysis, given the challenges of variations in size and shape. The survey focuses on enhancing the model's capabilities through data augmentation. Additionally, it delves into the intricate realm of recognizing handwritten numbers, presenting relevant work and emphasizing the use of various methods such as neural networks, k-nearest neighbor (KNN), and Support V

APA, Harvard, Vancouver, ISO, and other styles

26

Ishan, Gulati*1 Gautam Vig2 &. Vijay Khare3. "REAL TIME HANDWRITTEN CHARACTER RECOGNITION USING ANN." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 357–62. https://doi.org/10.5281/zenodo.1218609.

Full text

Abstract:

<em>-</em>Real time  Handwritten Character Recognition by using Template Matching is a system which is useful to recognize the character or alphabets in the given text by comparing two images of the alphabet. The objectives of this system prototype are to develop a program for the Optical Character Recognition (OCR) system by using the Template Matching algorithm . Handwritten character recognition is a challenging task in the field of research on image processing, artificial intelligence as well as machine vision since the handwriting varies from person to person. Moreover, the handwriti

APA, Harvard, Vancouver, ISO, and other styles

27

Skoropadskaya, Anna A. "The “fathers and children” theme in Ivan Shmelyov’s story." Tekst. Kniga. Knigoizdanie, no. 36 (2024): 21–34. https://doi.org/10.17223/23062061/36/2.

Full text

Abstract:

The article examines how Ivan Shmelyov develops the “fathers and children” theme in the process of creating the story The Man from the Restaurant. The main research method is comparative textual analysis. The material for the study was the surviving handwritten and printed draft versions of the story. The topicality of the research is determined by the need for a more detailed study of Shmelyov’s early works in order to clarify the origins of the author’s method, which was defined as “spiritual realism” and which genetically goes back to neorealism. The novelty of the research lies in the anal

APA, Harvard, Vancouver, ISO, and other styles

28

O, Hyon-Gwang, Myong-Chol Kim, Il-Nam Pak, Un-Hyok Choe, and Chol-Jun O. "RanPil: New Dataset and Benchmark for Offline Handwritten Korean Text Recognition." International Journal on Data Science and Technology 11, no. 2 (2025): 27–34. https://doi.org/10.11648/j.ijdst.20251102.12.

Full text

Abstract:

In recent years, since deep learning technology have been applied to handwritten text recognition, the need for handwritten document image Datasets has been growing more and more. In particular, the development of the dataset is of great significance for improving performance of handwritten Korean text recognition because no dataset for handwritten Korean text recognition has been published. In this paper, we present the “RanPil”, a new training and performance evaluation dataset for handwritten Korean text recognition, which consists of a total of 8,600 pages of images (182,000 text lines and

APA, Harvard, Vancouver, ISO, and other styles

29

Anand, Akash, Akshay Anand Rastogi, Rohit A. Chadichal, Anshul Surana, Dr Shyamala G, and Dr Latha N. R. "Handwritten Text Recognition and Conversion to Speech." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3904–14. http://dx.doi.org/10.22214/ijraset.2023.54317.

Full text

Abstract:

Abstract: Handwritten text recognition and conversion to speech is a complex task that involves multiple stages and technologies. The process begins with image processing, where the handwritten text is captured and pre-processed to enhance its quality and remove any noise. The next step is to perform optical character recognition (OCR), which involves recognizing individual characters in the text and converting them into a digital form that can be processed by a computer. Once the text has been digitized, it is processed by natural language processing (NLP) algorithms to identify and extract r

APA, Harvard, Vancouver, ISO, and other styles

30

Chandrakala, M. "Image Analysis of Sauvola and Niblack Thresholding Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2353–57. http://dx.doi.org/10.22214/ijraset.2021.34569.

Full text

Abstract:

Image segmentation is a critical problem in computer vision and other image processing applications. Image segmentation has become quite challenging over the years due to its widespread use in a variety of applications. Image thresholding is a popular image segmentation technique. The segmented image quality is determined by the techniques used to determine the threshold value.A locally adaptive thresholding method based on neighborhood processing is presented in this paper. The performance of locally thresholding methods like Niblack and Sauvola was demonstrated using real-world images, print

APA, Harvard, Vancouver, ISO, and other styles

31

Nilam, Mistry* Sameer Vashi Vidhi Patel Kunal Shah Denish Rixawapla Foram Rakholiya Rakesh Savant. "A REVIEW ON SEGMENTATION TECHNIQUES OF LINES, WORDS AND CHARACTERS ON GUJARATI HANDWRITTEN DOCUMENT USING OCR." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 6 (2016): 199–208. https://doi.org/10.5281/zenodo.54779.

Full text

Abstract:

OCR is technique to convert the handwritten or printed document into the digital format by scanning it which can be understandable by a computer. OCR is important and challenging task in many computer vision applications. Segmentation is generally the first stage in any attempt to analyse or interpret an image automatically.  Segmentation is separate the document into lines, lines to words and words to characters which has been one of the major laboriousness in handwritten text recognition. The role of segmentation is a crucial in most tasks requiring image analysis. The success or failur

APA, Harvard, Vancouver, ISO, and other styles

32

Sharma, Kartik, S. V. Jagadeesh Kona, Anshul Jangwal, Aarthy M, Prayline Rajabai C, and Deepika Rani Sona. "Handwritten Digits and Optical Characters Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 20–24. http://dx.doi.org/10.17762/ijritcc.v11i4.6376.

Full text

Abstract:

The process of transcribing a language represented in its spatial form of graphical characters into its symbolic representation is called handwriting recognition. Each script has a collection of characters or letters, often known as symbols, that all share the same fundamental shapes. Handwriting analysis aims to correctly identify input characters or images before being analysed by various automated process systems. Recent research in image processing demonstrates the significance of image content retrieval. Optical character recognition (OCR) systems can extract text from photographs and tra

APA, Harvard, Vancouver, ISO, and other styles

33

Rakesh, S., P. Kushal Reddy, V. Prashanth, and K. Srinath Reddy. "Handwritten text recognition using deep learning techniques: A survey." MATEC Web of Conferences 392 (2024): 01126. http://dx.doi.org/10.1051/matecconf/202439201126.

Full text

Abstract:

HTR (Handwritten Text Recognition) is the automated process of converting handwritten text into digital text, holding immense value in digitizing historical records and facilitating data entry. Through a combination of image processing and HTR systems decode handwritten characters and words. Pre-processing techniques increases image quality by reducing noise and correcting orientation, while models, like “convolutional neural networks” and “recurrent neural networks”, extract features and capture sequence patterns. Effective HTR models demand diverse training datasets and involve supervised le

APA, Harvard, Vancouver, ISO, and other styles

34

V.Khangar, Smita, and Latesh G. Malik. "Handwritten Text Image Compression for Indic Script Document." International Journal of Computer Applications 47, no. 5 (2012): 11–16. http://dx.doi.org/10.5120/7183-9888.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Khudhair, Khamael A., and Itimad Raheem Ali. "Using Feature Extraction to Recognize Handwritten Text Image." International Journal of Scientific & Engineering Research 5, no. 1 (2014): 89–95. http://dx.doi.org/10.14299/ijser.2014.01.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Newalkar, Akshat, Himanshu Khade, Dhiraj Khandare, and Divy Patel. "CNN-Powered Handwriting to Digital Text Converter." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 5 (2024): 355–66. https://doi.org/10.5281/zenodo.14016761.

Full text

Abstract:

The technology has become a crucial component in the digital transformation of documents for banks, educational institutions, and other sectors. In this paper, we design a handwritten character-to-text converter using CNN, where the input is a handwritten character and it is converted into computerized text. You see, CNNs are pretty darn good at image processing, and basically, what we're doing in the input debugger is detecting individual characters from a wide range of horrible handwritten gibberish. The model is trained on a dataset of handwritten characters, and its hierarchical feature ex

APA, Harvard, Vancouver, ISO, and other styles

37

Shafique, A. Awan, Nawaz Hakro Dil, Lashari Intzar, H. Jalbani Akhtar, and Hameed Maryam. "A Complete Off-line Sindhi Handwritten Text Recognition: A Survey." International Journal of Management Sciences and Business Research 6, no. 4 (2017): 131–38. https://doi.org/10.5281/zenodo.3469359.

Full text

Abstract:

Artificial Intelligence is finding ways to make machines more intelligent and work like human being. Image processing, Natural language processing and Optical Character Recognition (OCR) are the active fields of computer vision, where the computers are made more versatile to understand, read and write natural human languages spoken around the word. Optical Characters Recognition (OCR) and Intelligent Characters Recognition (ICR) differ in recognizing printed and handwritten characters respectively. Intelligent Characters Recognition (ICR) is an active field in which handwritten characters are

APA, Harvard, Vancouver, ISO, and other styles

38

N D, Sukesh, and Steephan Amalraj J. "Handwritten Character Recognition Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25945.

Full text

Abstract:

Handwritten digit or character recognition in transforming the printed or handwritten text from an image. Optical character recognition plays an important role in documentation scanning ,text extractions from the image. Optical character recognition is used in different fields like postal services ,Ecommerce , Shipping ,Banking sector for character extraction from the images . However the existing character recognition system faces many challenges in extracting text from noisy and distortion images or complex layout and Extraction mostly limited to numbers and English alphabets . The introduct

APA, Harvard, Vancouver, ISO, and other styles

39

Maiti, Somanka, Shabari Nath Panuganti, Gaurav Bhatnagar, and Jonathan Wu. "Efficient Image Inpainting for Handwritten Text Removal Using CycleGAN Framework." Mathematics 13, no. 1 (2025): 176. https://doi.org/10.3390/math13010176.

Full text

Abstract:

With the recent rise in the development of deep learning techniques, image inpainting—the process of restoring missing or corrupted regions in images—has witnessed significant advancements. Although state-of-the-art models are effective, they often fail to inpaint complex missing areas, especially when handwritten occlusions are present in the image. To address this issue, an image inpainting model based on a residual CycleGAN is proposed. The generator takes as input the image occluded by handwritten missing patches and generates a restored image, which the discriminator then compares with th

APA, Harvard, Vancouver, ISO, and other styles

40

Kang, Wenjing, and Wenbo Li. "Enhanced diffusion model based on similarity for handwritten digit generation." Applied and Computational Engineering 32, no. 1 (2024): 129–35. http://dx.doi.org/10.54254/2755-2721/32/20230199.

Full text

Abstract:

In recent years with the rise of deep learning, there has been a major revolution in image generation technology. Deep learning models, especially the diffusion model. have brought about breakthrough progress in image generation. Various deep generation models have recently demonstrated a wide variety of high-quality sample data patterns. Although image generation technology has achieved remarkable achievement. There are still challenges and issues, such as quality control in generated images. In order to improve the robustness and performance of diffusion model in image generation, an enhance

APA, Harvard, Vancouver, ISO, and other styles

41

Shrawankar, Urmila. "Standardization of Handwritten Words to Improve Readability." International Journal of Technology Diffusion 10, no. 3 (2019): 1–17. http://dx.doi.org/10.4018/ijtd.2019070101.

Full text

Abstract:

Everyone has different handwriting, and this is a difficulty since not everyone can discern the handwriting of different people. The described technique converts unstructured handwriting into a structured type. To resolve the problem, the authors want to separate every letter of the words so as to converting the alphabet into a commonplace type. This is to simplify the written language for everyone. This article presents a piece of text identification from image. OCR is enforced to convert electronic kind of image into machine-editable text.

APA, Harvard, Vancouver, ISO, and other styles

42

KAVALLIERATOU, E., N. DROMAZOU, N. FAKOTAKIS, and G. KOKKINAKIS. "AN INTEGRATED SYSTEM FOR HANDWRITTEN DOCUMENT IMAGE PROCESSING." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 04 (2003): 617–36. http://dx.doi.org/10.1142/s0218001403002538.

Full text

Abstract:

In this paper we attempt to face common problems of handwritten documents such as nonparallel text lines in a page, hill and dale writing, slanted and connected characters. Towards this end an integrated system for document image preprocessing is presented. This system consists of the following modules: skew angle estimation and correction, line and word segmentation, slope and slant correction. The skew angle correction, slope correction and slant removing algorithms are based on a novel method that is a combination of the projection profile technique and the Wigner–Ville distribution. Furthe

APA, Harvard, Vancouver, ISO, and other styles

43

Souibgui, Mohamed Ali, Sanket Biswas, Andres Mafla, et al. "Text-DIAE: A Self-Supervised Degradation Invariant Autoencoder for Text Recognition and Document Enhancement." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2330–38. http://dx.doi.org/10.1609/aaai.v37i2.25328.

Full text

Abstract:

In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Each of the pretext objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that confirm the design choice of the selected pretext tasks. Importantl

APA, Harvard, Vancouver, ISO, and other styles

44

Papavassiliou, Vassilis, Themos Stafylakis, Vassilis Katsouros, and George Carayannis. "Handwritten document image segmentation into text lines and words." Pattern Recognition 43, no. 1 (2010): 369–77. http://dx.doi.org/10.1016/j.patcog.2009.05.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Asghar, Ali Chandio, Leghari Mehwish, Orangzeb Panhwar Ali, Zaman Nizamani Shah, and Leghari Mehjabeen. "Deep learning-based isolated handwritten Sindhi character recognition." Indian Journal of Science and Technology 13, no. 25 (2020): 2565–74. https://doi.org/10.17485/IJST/v13i25.914.

Full text

Abstract:

Abstract <strong>Motivation :</strong> The problem of handwritten text recognition is vastly studied since last few decades. Many innovative ideas have been developed, where state-of-the-art accuracy is achieved for the English, Chinese or Indian scripts.The recent developments for the cursive scripts such as Arabic and Urdu handwritten text recognition have achieved remarkable accuracy. However, for the Sindhi script, existing systems have not shown significant results and the problem is still an open challenge. Several challenges such as variations in writing styles, joined text, ligatu

APA, Harvard, Vancouver, ISO, and other styles

46

Iskandar, Nasuha, Wei Jen Chew, and Swee King Phang. "The Application of Image Processing for Conversion of Handwritten Mathematical Expression." Journal of Physics: Conference Series 2523, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2523/1/012014.

Full text

Abstract:

Abstract Optical character recognition (OCR) is the conversion of printed or written text from a scanned document or image file into a machine-readable form to be used for data processing like editing. Handwriting has been a way of communication for centuries, but modern technology has made it easier with the introduction of modern computers. While people have adapted to typing out words using a keyboard, formulas and mathematical expressions requires additional add-ons installed in the word processor. The process can be time consuming and tedious. Therefore, an alternative method is proposed

APA, Harvard, Vancouver, ISO, and other styles

47

Refaey, Mohammed A. A. "Background Ruled-Lines Detection and Removal in Full-Colored Handwritten Image Documents." International Journal of Image and Graphics 15, no. 02 (2015): 1540006. http://dx.doi.org/10.1142/s0219467815400069.

Full text

Abstract:

Automation becomes the standard in nearly all aspects of life. Some of these aspects are text analysis, translating and retrieval. This requires machine typed format as a preprocessing step. Converting the handwritten text into machine printed counterpart requires Optical Character Recognition (OCR) system, which requires clean text as input. One of the problems facing the process of getting clean handwritten text is the ruled background lines which are intersecting and mixed with the text. In this work, we present fast algorithms for detection and removal of these ruled lines. The detection s

APA, Harvard, Vancouver, ISO, and other styles

48

Chen, Aldyn. "Handwritten Text Classification Based on Convolutional Neural Network." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 39–44. http://dx.doi.org/10.54097/hset.v34i.5372.

Full text

Abstract:

The convolutional neural network (CNN) is a popular and highly effective deep learning technique for image classification. As the popularity of CNNs grew, the model has become popular in several machine learning problems. This paper utilizes a CNN model and the popular LeNet-5 transfer learned model to classify texts after the words are preprocessed and segmented from an image. The EMNIST database is used to train the models. The paper achieves an 89.36% validation accuracy on the EMNIST Balanced dataset and an 86.64% on the EMNIST By_Class dataset for the CNN model of four convolutional layer

APA, Harvard, Vancouver, ISO, and other styles

49

Athoillah, Muhammad. "K-Nearest Neighbor for Recognize Handwritten Arabic Character." Jurnal Matematika "MANTIK" 5, no. 2 (2019): 83–89. http://dx.doi.org/10.15642/mantik.2019.5.2.83-89.

Full text

Abstract:

Handwritten text recognition is the ability of a system to recognize human handwritten and convert it into digital text. Handwritten text recognition is a form of classification problem, so a classification algorithm such as Nearest Neighbor (NN) is needed to solve it. NN algorithms is a simple algorithm yet provide a good result. In contrast with other algorithms that usually determined by some hypothesis class, NN Algorithm finds out a label on any test point without searching for a predictor within some predefined class of functions. Arabic is one of the most important languages in the worl

APA, Harvard, Vancouver, ISO, and other styles

50

Patel, Dr Bharat C., and Dr Jagin M. Patel. "Comparative Study on Text Segmentation Techniques." YMER Digital 21, no. 01 (2022): 372–80. http://dx.doi.org/10.37896/ymer21.01/35.

Full text

Abstract:

Text segmentation, whether printed, handwritten or cursive, is one of the most complicated phases in any OCR. The accuracy of recognition will be heavily reliant on good segmentation. Image segmentation is a crucial component of image analysis and the field of computer vision. Researchers have developed several techniques for segmentation, each of which is used for different types of segmented objects. At present no any universal method is available for image segmentation. Existing image segmentation techniques are not capable to deal with images of any types. This survey looked at a variety o

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!