Статті в журналах: "Handwriting text recognition"

1

Devaraj, Anjali Yogesh, Anup S. Jain, Omisha N, and Shobana TS. "Kannada Text Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 73–78. http://dx.doi.org/10.22214/ijraset.2022.46520.

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Abstract: The task of automatic handwriting recognition is critical. This can be a difficult subject, and it has gotten a lot of attention in recent years. In the realm of picture grouping, handwritten character recognition is a problem. Handwritten characters are difficult to decipher since various people have distinct handwriting styles. For decades, researchers have been focusing on character identification in Latin handwriting. Kannada has had fewer studies conducted on it. Our "Kannada Text Recognition" research and effort attempts to classify and recognize characters written in Kannada, a south Indian language. The characters are taken from written documents, preprocessed with numpy and OpenCV, and then run through a CNN.

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2

Tran, Dat, Wanli Ma, and Dharmendra Sharma. "Handwriting Recognition Applications for Tablet PCs." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 7 (September 20, 2007): 787–92. http://dx.doi.org/10.20965/jaciii.2007.p0787.

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This paper presents handwriting recognition applications developed and tested on the tablet PC – a new generation of notebook computers. Users write on a tablet PC screen with a tablet pen and a built-in user-independent handwriting recognition tool converts handwritings to printed text. We present handwriting recognition applications using the built-in recognition tool and signature verification using our own verification tool based on fuzzy c-means vector quantization (FCMVQ) and observable Markov modeling (OMM). Experimental results for the signature verification system are also presented.

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3

Xiong, Yu-Jie, Li Liu, Shujing Lyu, Patrick S. P. Wang, and Yue Lu. "Improving Text-Independent Chinese Writer Identification with the Aid of Character Pairs." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 02 (October 24, 2018): 1953001. http://dx.doi.org/10.1142/s021800141953001x.

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Text-independent Chinese writer identification does not depend on the text content of the query and reference handwritings. In order to deal with the uncertainty of the text content, text-independent approaches usually give special attention to the global writing style of handwriting, rather than the properties of each individual character or word. Thanks to the existence of high-frequency characters, some characters probably appear in both the query and reference handwritings in most cases. If character images in the query handwriting are similar to those in the reference handwriting, this query handwriting and the corresponding reference handwriting are very likely to be written by the identical writer. In this paper, we exploit the above characteristic to improve the performance of Chinese writer identification. We first present an identification scheme using edge co-occurrence feature (ECF). Then, we detect the character pairs in the query and reference handwritings using a two-step framework and propose the displacement field-based similarity (DFS) to determine whether a character pair is written by the identical writer. The character pairs help to re-rank the candidate list obtained by text-independent ECF-based similarity and finally decide the writer of the query handwriting. The proposed method is evaluated on the HIT-MW and CASIA-2.1 datasets. Experimental results demonstrate that our proposed method outperforms the existing ones, and its Top-1 accuracy on the two datasets reaches 97.1% and 98.3%, respectively.

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4

Ram Kumar, R. P., A. Chandra Prasad, K. Vishnuvardhan, K. Bhuvanesh, and Sanjeev Dhama. "Automated Handwritten Text Recognition." E3S Web of Conferences 430 (2023): 01022. http://dx.doi.org/10.1051/e3sconf/202343001022.

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A computer’s capacity to recognize and convert handwritten inputs from sources like photographs and paper documents into digital format is known as Automated Handwritten Text Recognition (AHTR). Systems for reading handwriting are frequently employed in a variety of fields, including banking, finance, and the healthcare industry. In this paper, we took on the problem of categorizing any handwritten artwork, whether it be in block lettering or cursive. There are many different types of handwritten characters, including digits, symbols, and scripts in both English and other languages. This makes the evolution of handwriting more complex. It is difficult to train an Optical Character Recognition (OCR) system using these requirements. In order to convert handwritten material into digital form, this work aims to categorize each unique handwritten word. Because Convolutional Neural Networks (CNNs) are so good at this task, they are the best method for handwriting recognition system. The method will be used to identify writings in various formats.

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5

Bazarkulova, Aisaule. "KAZAKH HANDWRITING RECOGNITION." Suleyman Demirel University Bulletin Natural and Technical Sciences 62, no. 1 (October 15, 2024): 88–102. https://doi.org/10.47344/sdubnts.v62i1.963.

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Recognition of handwritten text is one aspect of objectrecognition and known as handwriting detection cause of a computer’spotential to recognize and comprehend readable handwriting from resourcesincluding paper files, touch smart devices, images, etc. Data is categorized intoa number of classes or groups using pattern recognition. The paper presents asuccessful experiment in recognizing handwritten Kazakh text usingConvolutional Recurrent Neural Network based architectures and the KazakhAutonomous Handwritten Text Dataset. The proposed algorithm achieved anoverall accuracy of 86.36% and showed promising results. However, the papersuggests that further research could be conducted to improve the model, suchas correlating and enlarging the database or incorporating other models andlibraries. Additionally, the paper emphasizes the importance of consideringlanguage specifics when building a text recognition model, as modernalgorithms that work well in one language may not guarantee the sameperformance in another.

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Dilmurat, Halmurat, and Kurban Ubul. "Design and Realization of On-Line Uyghur Handwritten Character Collection System." Advanced Materials Research 989-994 (July 2014): 4742–46. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4742.

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Data collection is the first step in handwritten character recognition systems, and the data quality collected effects the whole systems efficiency. As the necessary subsystem of on-line handwritten character/word recognition system, a Uyghur handwritten character collection system is designed and implemented with Visual C++ based on the nature of Uyghur handwriting. Uyghur handwritings is encoded by 8 direction tendency and stored in extension stroke file. And they are collected based on the content of Text Prompt File. From experimental results, it can be concluded that the handwriting collection system indicates its strong validity and efficiency during the collection of Uyghur handwriting.

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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 (June 2022): 455–64. http://dx.doi.org/10.18287/2412-6179-co-1049.

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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 used, they could also be applied to enhance the performance of other networks and approaches to handwriting text recognition. Extensive experiments on ten handwritten text datasets show that HandWritten Blots augmentation and StackMix significantly improve the quality of handwriting text recognition models.

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Kaur, Amrit Veer, and Amandeep Verma. "Hybrid Wavelet based Technique for Text Extraction from Images." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 9 (October 31, 2017): 24. http://dx.doi.org/10.23956/ijarcsse.v7i9.406.

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This paper reviews the current state of the art in handwriting recognition research. The paper deals with issues such as hand-printed character and cursive handwritten word recognition. It describes recent achievements, difficulties, successes and challenges in all aspects of handwriting recognition.

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Pittman, James A. "Handwriting Recognition: Tablet PC Text Input." Computer 40, no. 9 (September 2007): 49–54. http://dx.doi.org/10.1109/mc.2007.314.

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10

Kumar, J., and A. Roy. "DograNet – a comprehensive offline dogra handwriting character dataset." Journal of Physics: Conference Series 2251, no. 1 (April 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2251/1/012008.

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Abstract Handwritten Text Recognition is an important area of research because of growing demand to process and convert a huge data and information available in handwritten form to Digital form. The digital data instead of handwritten form can prove to be highly useful in different fields. Handwritten text recognition plays an important role in applications involved in, postal services, banks for cheque processing, searching of information and organization dealing with such applications. In text recognition application dataset of the specified script is required for training purpose. Datasets of the different languages could be found online but dataset of dogra script characters is still not available. This paper presents a Dogra handwriting character dataset which contains around 38690 character images etc grouped in 73 character classes extracted from 530 one-page handwritings of 265 individuals of having variable age, sex, qualification, location. The dogra character dataset would be freely accessible by scholars and researchers which could also be used for further recognition improvement and updating with more characters and word, Identification of writer, dogra word segmentation. Dogra dataset could also be used for extracting variation of handwriting according to age and gender.

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Mohammed, Mamoun Jassim, Suphian Mohammed Tariq, and Hayder Ayad. "Isolated Arabic handwritten words recognition using EHD and HOG methods." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (May 1, 2021): 801. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp801-808.

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<span>Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.</span>

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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.

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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 StackMix method of gluing words from symbols, and the use of a generative adversarial network. A font-based method for creating images of handwritten text in Russian has been developed and described in this work. In addition, an algorithm for the formation of a new Cyrillic handwritten font based on the existing images of handwritten characters is proposed. The effectiveness of the developed method was tested using experiments that were carried out on two publicly available Cyrillic datasets using two different recognition models. The results of the experiments showed that the developed method for generating images made it possible to increase the accuracy of handwriting recognition by an average of 6%, which is comparable to the results of other more complex methods. The source code of the experiments, the proposed method, as well as the datasets generated during the experiments are posted in the public domain and are ready for download.

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Ramdan, Jabril, Khairuddin Omar, Mohammad Faidzul, and Ali Mady. "Arabic Handwriting Data Base for Text Recognition." Procedia Technology 11 (2013): 580–84. http://dx.doi.org/10.1016/j.protcy.2013.12.231.

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14

Maddineni, Bhavyasri. "Various Models for the Conversion of Handwritten Text to Digital Text." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 2894–99. http://dx.doi.org/10.22214/ijraset.2021.35616.

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Handwritten Text Recognition (HTR) also known as Handwriting Recognition (HWR) is the detection and interpretation of handwritten text images by the computer. Handwritten text from various sources such as notebooks, documents, forms, photographs, and other devices can be given to the computer to predict and convert into the Computerized Text/Digital Text. Humans find easier to write on a piece of paper rather than typing, but now-a-days everything is being digitalized. So, HTR/HWR has an increasing use these days. There are various techniques used in recognizing the handwriting. Some of the traditional techniques are Character extraction, Character recognition, and Feature extraction, while the modern techniques are segmenting the lines for recognition, machine learning techniques, convolution neural networks, and recurrent neural networks. There are various applications for the HTR/HWR such as the Online recognition, Offline Recognition, Signature verification, Postal address interpretation, Bank-Cheque processing, Writer recognition and these are considered to be the active areas of research. An effective HTR/HWR is therefore needed for the above stated applications. During this project our objective is to find and develop various models of the purpose.

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Fitriana, Gita Fadila. "Pengenalan Tulisan Tangan Angka menggunakan Self Organizing Maps (SOM)." Building of Informatics, Technology and Science (BITS) 3, no. 1 (June 30, 2021): 31–42. http://dx.doi.org/10.47065/bits.v3i1.1002.

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Handwriting is character pattern recognition. Character pattern recognition is exciting to do research. In character pattern recognition, many types of characters can be recognized by computers and solved by various algorithms. Various kinds of pattern recognition have been successfully applied in multiple fields such as voice recognition, face detection, fingerprint recognition, and handwriting recognition. Handwriting recognition is divided into two types, namely online handwriting recognition and offline handwriting recognition. Online handwriting recognition requires special electronic equipment, and handwriting is captured on a pressure-sensitive tablet. Offline handwriting recognition does not need a particular machine because handwriting data is entered from previously written text such as images scanned by a scanner. Several methods have been developed to recognize handwriting with varying degrees of accuracy. This research uses the feature extraction of United Moment Invariant (UMI) and Self Organizing Maps (SOM). Based on the results of the software experiment for the entire test data set, the primary data yielded an accuracy rate of 88% for 50 images, and the first secondary data paid an accuracy rate of 98.2% for 500 images. However, for the second secondary data experiment with 50 test data, the accuracy rate is 90%. The third secondary data experiment was 500 test data. The accuracy rate was 89%. When viewed from the accuracy value, the primary data has a lower level of accuracy when compared to the two secondary data with different amounts. The story of accuracy resulting from experimenting with varying data sets proves that handwritten characters have a high and inconsistent level of variation. This is caused by the thickness and form of writing that is not consistent in each person and habits that affect the character of one's handwriting. Primary data is data that is taken directly and through a scanner process and still has a lot of noise in the handwritten image of numbers. At the same time, the secondary data has undergone a grey image process so that the handwritten image is clean from noise.

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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 (October 1, 2021): 2457–63. http://dx.doi.org/10.21917/ijsc.2021.0351.

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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 recognition, signature verification, and various others. In this project, offline handwritten Text recognition is performed using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) which uses the architecture of Recurrent Neural network (RNN) and connectionist temporal classification (CTC). The neural network is trained and tested using the IAM database containing handwritten English text. The implementation of this work is done using image segmentation-based handwritten text recognition where OpenCV is used for performing image processing and TensorFlow is used for training and text recognition. This whole system is developed using python and the output is displayed in a word file.

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17

Hong, Chen, Gareth Loudon, Yimin Wu, and Ruslana Zitserman. "Segmentation and Recognition of Continuous Handwriting Chinese Text." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 02 (March 1998): 223–32. http://dx.doi.org/10.1142/s0218001498000154.

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This article introduces the basic segmentation problems in Chinese handwriting and also several prior work to solve these problems. A new segmentation method is proposed, which is applicable to both on-line and off-line systems for free-format handwritten Chinese character sentences. This method performs basic segmentation and fine segmentation based on the varying spacing thresholds and the minimum variance criteria. The five most probable ways of segmentation are derived from this stage and all the possible segments are extracted and recognized. A lattice is created from all the segments and searched using a viterbi based algorithm to find the most likely character sequence. The algorithm presented in this paper provides large flexibility and robustness to handle free-format continuous Chinese handwriting and is a promising solution for a natural and fast Chinese pen input system. The character accuracy is 85.0% for on-line and 77.4% for the off-line test data.

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18

Huynh, Loc Huu, Hai Quoc Luu, and Vu Duc Anh Dinh. "MODIFIED DIRECTION FEATURE AND NEURAL NETWORK BASED TECHNIQUE FOR HANDWRITING CHARACTER RECOGNITION." Science and Technology Development Journal 14, no. 2 (June 30, 2011): 62–70. http://dx.doi.org/10.32508/stdj.v14i2.1910.

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Handwriting character recognition is an important research topic which has various applications in surveillance, radar, robot technology... In this paper, we propose the implementation of the handwriting character recognition using off-line handwriting recognition. The approach consists of two steps: to make thin handwriting by keeping the skeleton of character and reject redundant points caused by humam’s stroke width and to modify direction method which provide high accuracy and simply structure analysis method to extract character’s features from its skeleton. In addition, we build neural network in order to help machine learn character specific features and create knowledge databases to help them have ability to classify character with other characters. The recognition accuracy of above 84% is reported on characters from real samples. Using this off-line system and other parts in handwriting text recognition, we can replace or cooperate with online recognition techniques which are ususally applied on mobile devices and extend our handwriting recognition technique on any surfaces such as papers, boards, and vehicle lisences as well as provide the reading ability for humanoid robot.

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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 (May 4, 2023): 20–24. http://dx.doi.org/10.17762/ijritcc.v11i4.6376.

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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 transform that text to ASCII text. OCR is beneficial and essential in many applications, such as information retrieval systems and digital libraries.

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Vishnoi, Gaurvi, Rahul Bansal, Arpit Garg, and Atyab Tosif. "Full Page Handwriting Recognition on CUDA enabled Docker." Journal of Artificial Intelligence and Imaging 1, no. 2 (October 23, 2024): 26–33. http://dx.doi.org/10.48001/joaii.2024.1226-33.

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-Handwritten text recognition is essential for document digitization but often struggles with multiline content. This paper presents an integrated approach using TrOCR, a pre-trained Transformer model, combined with PaddleOCR for enhanced text detection. The integration, optimized with GPU acceleration and multi-threading within a CUDA-enabled Docker environment, addresses the challenges of full-page handwriting recognition. A user-friendly Flask API with a Gradio demo was developed for deployment. Experimental results demonstrate that the system significantly improves the accuracy of multiline text recognition, outperforming existing models. This approach offers a scalable, efficient solution for accurate handwriting recognition in complex document layouts, advancing the field of document digitization.

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Shrestha, Ranila, Oshin Shrestha, Monika Shakya, Urja Bajracharya, and Subash Panday. "Offline Handwritten Text Extraction and Recognition Using CNN-BLSTM-CTC Network." International Journal on Engineering Technology 1, no. 1 (December 21, 2023): 166–80. http://dx.doi.org/10.3126/injet.v1i1.60941.

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Offline handwriting recognition is a significant research area that aims at tackling problems encountered with handwritten forms in college application and registration processes. The objective of this study is to address the problems of English language offline handwriting recognition via CNN-BLSTM-CTC neural network applied for an NCE Admission form. The system uses OpenCV for image processing, TensorFlow for neural network training and handwritten text recognition, and trains and tests it on the IAM database using image segmentation-based handwriting recognition. With the help of proper image verification, the system allows the users to upload images of the NCE Admission form provided that they strictly comply with the specified format; it denies access to images not conforming to the set standards. Following the successful delivery of a valid image, the form goes through extensive processing that includes text extraction from specific regions of interest (ROIs). The extracted texts are then passed to text recognition block. The recognized texts are then recorded in a CSV file under respective fields. The text recognition model has a CER of approximately 9.33%. The study performed with 15 NCE Admission forms found that the average Character Error Rate (CER) was approximately 12.2% for scanned images and 19.3% for camera-captured images. The results show that accuracy depends on aspects such as the quality and orientation of the image; thus, scanned images are preferred for better performance.

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Dhotre, Ketaki G., Harshali K. Ghumate, Mayuri Mane, and Prof Savita Lade. "Handwriting to Text Conversion for English Language Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1346–51. http://dx.doi.org/10.22214/ijraset.2022.40876.

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Abstract: Because of the rising use of digital technology in all businesses and in all day-to-day activities to store and communicate information, recognition systems in writing have become a prominent study topic and development. Humans still require handwriting copies to be converted into digital copies that can be shared and preserved electronically. Handwriting recognition is one of the most active study areas, and deep neural networks are being used in it. Humans find it simple to recognise handwriting, but computers find it tough. Nowadays, technologies that detect handwriting letters, characters, and figures assist people in doing more sophisticated activities that would otherwise take a long time and be costly. The purpose of this project is to turn handwritten notes into typed documents. We aim to transform handwritten English characters into a computer-readable format using a paragraph as an input, process the paragraph with cursive writing and symbols support, and then train a neural network algorithm to recognize and display the text. CNN is the neural network model that we used. The image can be uploaded by the user. To eliminate background noise, the system pre-processes the input. The machine then looks for text sections in the image. The system then displays the text that is contained in the image to the user. To conduct horizontalvertical segmentation, we used OpenCV. Keywords: Handwriting, Bi-LSTM, Convolutional Neural Network, Text Conversion, Deep Learning, OpenCV

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Sable, Prof A. V., Avantika Patil, Mayur Rathi, and Ayush Shriwas. "Interpreting Doctor Notes using Handwriting Recognition." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 3118–23. http://dx.doi.org/10.22214/ijraset.2024.60663.

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Abstract: Handwriting recognition of medical prescriptions has been a challenging problem over the recent years with constant research in providing possible accurate solutions. Indecipherable handwritten prescription and inefficiency of Pharmacist to understand the medical prescription can lead to serious and harmful effect to the patients. Even in the recognition of handwriting, mainly doctors notes, they are very difficult for everyone to understand and it takes time for a person to analyse it. So, this idea mainly focused on interpreting doctor’s notes using handwritten recognition and deep learning techniques. The handwritten or printed document pictures are transformed into their electronic counterparts using an optical character recognition (OCR) system. Due to individuals' inconsistent writing styles, dealing with handwritten texts is significantly more difficult than dealing with printed ones. Handwritten text recognition could be done by Image processing, Machine Learning or Deep Learning Techniques. Out of these Deep Learning remains to be the most popular and prominent. Some of the Deep Learning techniques includes Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). This gives a review of the various recognition methodologies used for interpreting handwritten texts. It includes the most important algorithms that could be used for detecting the handwritten word/text/character by using various approaches for the recognition process. In the end we are thus comparing the accuracies provided by these systems.

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Schüler, Lisa. "Schreibflüssigkeit im Medienvergleich: Handschrift – Tastaturschreiben – Diktieren mit Spracherkennung." Zeitschrift für Angewandte Linguistik 75, no. 1 (August 6, 2021): 330–63. http://dx.doi.org/10.1515/zfal-2021-2077.

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Abstract Fluency is considered an essential prerequisite for successful text production. Writing fluency means mastering the basic processes of writing so that cognitive resources are freed up to concentrate on content planning or revision when writing texts. Although the importance of these basic processes is well known, there has been little research on this in a German language context. What is known, however, is that digital technologies can support written production. Compared to handwriting, typing is less demanding in terms of motor skills, for example. Dictation in conjunction with speech recognition in turn supports text production not only in the area of motor execution, but also in the area of spelling. In order to capture the characteristics and currently possible potential of dictation with speech recognition, this study investigated this input mode and contrasted it with handwriting and typing in a writing fluency test (N = 46, 8th grade). The results show that the participants produce longer and more correct texts with the help of dictation with speech recognition than when handwriting or typing. However, there is also evidence that this new form of text production has its own challenges.

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Kaznin, A. "Recognition Handwriting and Printed Text for Software Requirements Engineering." Bulletin of Science and Practice 5, no. 12 (December 15, 2019): 246–56. http://dx.doi.org/10.33619/2414-2948/49/29.

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This article discusses the problems of collecting software requirements. The existing computer vision technologies are analyzed and the choice of technology for recognizing handwritten and printed text is justified. The input data for the experiments are described and the results of character recognition for each image category are presented. A method of image preprocessing and recognition of text characters on mobile devices using parallel computing has been developed. On the basis of the proposed method, a prototype mobile application for collecting and digitalizing data obtained during the requirements engineering has been developed.

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Peña Saldarriaga, Sebastián, Christian Viard-Gaudin, and Emmanuel Morin. "Impact of online handwriting recognition performance on text categorization." International Journal on Document Analysis and Recognition (IJDAR) 13, no. 2 (January 16, 2010): 159–71. http://dx.doi.org/10.1007/s10032-009-0108-6.

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M. Butaev, Mikhail, Mikhail Yu. Babich, Igor I. Salnikovq, Alexey I. Martyshkin, Dmitry V. Pashchenko, and Dmitry A. Trokoz. "Neural Network for Handwriting Recognition." Nexo Revista Científica 33, no. 02 (December 31, 2020): 623–37. http://dx.doi.org/10.5377/nexo.v33i02.10798.

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Today, in the digital age, the problem of pattern recognition is very relevant. In particular, the task of text recognition is important in banking, for the automatic reading of documents and their control; in video control systems, for example, to identify the license plate of a car that violated traffic rules; in security systems, for example, to check banknotes at an ATM and in many other areas. A large number of methods are known for solving the problem of pattern recognition, but the main advantage of neural networks over other methods is their learning ability. It is this feature that makes neural networks attractive to study. The article proposes a basic neural network model. The main algorithms are considered and a programming model is implemented in the Python programming language. In the course of research, the following shortcomings of the basic model were revealed: low learning rate (the number of correctly recognized digits in the first epochs of learning); retraining - the network has not learned to generalize the knowledge gained; low probability of recognition - 95.13%.To solve the above disadvantages, various techniques were used that increase the accuracy and speed of work, as well as reduce the effect of network retraining.

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Jaiswal, Kunal, Avichal Suneja, Aman Kumar, Anany Ladha, and Nidhi Mishra. "Preprocessing Low Quality Handwritten Documents for OCR Models." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 2980–85. http://dx.doi.org/10.22214/ijraset.2023.50664.

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Abstract: Handwriting recognition using OCR (Optical Character Recognition) is a transformative technology that is rapidly changing the way we interact with handwritten documents. OCR technology has traditionally been used for scanning printed text, but with advancements in machine learning and computer vision, it is now possible to recognize and digitize handwritten text as well. This has immense practical implications, as it enables handwritten notes, letters, and documents to be easily searchable, editable, and shareable in digital formats[1]. However, despite its potential, handwriting recognition using OCR is still a relatively nascenttechnology and faces several challenges. One of the main challenges is the recognition of handwriting styles that vary widely across individuals and cultures. Another challenge isinterpreting handwriting that is difficult to read, such as cursive writing or handwritten notes withsmudges or crossed out words. Additionally, OCR software is highly dependent on the qualityof the input image, making it important to optimize the lighting and capture settings for accurate results.[2] As OCR technology continues to improve, it hasthe potential to revolutionize the way we interact with handwritten documents and usher in a new era of digital transformation.

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Chen, Rui, Bin Fang, and Patrick Shen-Pei Wang. "Chinese Handwriting Identification Method Based on Keyword Extraction." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 11 (March 31, 2017): 1753004. http://dx.doi.org/10.1142/s0218001417530044.

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Text-independent handwriting identification methods require that features such as texture are extracted from lengthy document image; while text-dependent handwriting identification methods require that the contents of the documents being compared are identical. In order to overcome these confinements, this paper presents a novel Chinese handwriting identification technique. First, Chinese characters are segmented from handwriting document, then keywords are extracted based on matching and voting of local features of character. Then the same-content keywords are used to build training sets, and these training sets of two documents are compared. Because the keywords are similar to signature, the handwriting identification problem is transformed into signature verification problem. Experiments on HIT-MW, HIT-SW and CASIA show this method outperforms many text-independent handwriting identification methods.

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Chen, Zhuo, Fei Yin, Xu-Yao Zhang, Qing Yang, and Cheng-Lin Liu. "MuLTReNets: Multilingual text recognition networks for simultaneous script identification and handwriting recognition." Pattern Recognition 108 (December 2020): 107555. http://dx.doi.org/10.1016/j.patcog.2020.107555.

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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 (January 20, 2020): 161. http://dx.doi.org/10.3991/ijim.v14i01.11593.

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<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 digits, Latin / Alphabet and Hiragana, capture an image or choose the image from gallery to scan the handwritten text from the image, use the live camera to detect the handwritten text real – time without capturing an image and could copy the results of the output from the off-line recognition and share it to other platforms such as notes, Email, and social media. </span>

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Paci, Hakik, Dorian Minarolli, Evis Trandafili, and Stela Paturri. "Albanian Handwritten Text Recognition using Synthetic Datasets and Pre-Trained Models." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 21 (May 15, 2024): 264–71. http://dx.doi.org/10.37394/23209.2024.21.25.

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Handwritten Text Recognition (HTR) has continuously attracted the focus of researchers to enable the integration of technology into our daily lives. Handwritten text recognition (HTR), a technology of considerable importance, takes a leading role in the analysis and digitization of various documents. This technology is important in facilitating the efficient use of handwritten documents, especially within academic, historical, and cultural contexts. The use of artificial intelligence in handwriting recognition offers a very good opportunity to achieve satisfactory results in this field, but to achieve good results a large dataset is needed. Creating a large dataset to train different AI models is a challenge for languages with limited resources such as the Albanian language. This paper aims to present a novel approach to the development of an HTR system for the Albanian language using an attention-based encoder-decoder architecture. The dataset used in the experiments is a synthetic dataset generated using deep learning techniques based on the English language dataset as they are both variants of the Latin alphabet. We enhanced the dataset with two letters specific to Albanian, (“ë” and “ç”). The usage of pre-trained English models for handwriting recognition improved our model’s performance. The results of the experiments are very promising and prove that our approach is efficient in recognizing handwriting in the Albanian language. This shows that the attention-based encoder-decoder architecture can be adopted for different languages with limited resources.

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Dr. A. Shaji George. "Handwriting Recognition Implementation: A Machine Learning Approach." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 02 (February 5, 2025): 144–49. https://doi.org/10.47392/irjaem.2025.0025.

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Handwritten text recognition, also referred to as handwritten character recognition, is a field of study that combines model recognition, computer vision, and artificial intelligence. In order to translate handwritten letters into relevant text and computer commands in real time, handwriting recognition systems use pattern matching. The properties of photographs and touch-screen devices can be acquired, detected, and converted into a machine-readable form by an algorithm that recognizes handwriting. An ensemble of bagged classification trees is one way to accomplish this. A bagged classification tree is an ensemble learning technique that helps to increase the efficiency and accuracy of machine learning algorithms by lowering the variance of a prediction model and addressing bias-variance trade-offs. The standard Kaggle digits dataset from (0-9) was utilised in this study to identify handwritten digits using a bagged classification method. And with an accuracy level of 0.8371, we finally came to a conclusion about the importance of the bagged classification strategy.

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Li, Zhenjiang, Qianxue Zhang, and Yiqun Wang. "Text Independent Writer Identification Based on Pre-training Model and Feature Fusion." Journal of Physics: Conference Series 2363, no. 1 (November 1, 2022): 012015. http://dx.doi.org/10.1088/1742-6596/2363/1/012015.

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Handwriting is one of the natural biological characteristics of human beings. People have a high degree of acceptance of identity recognition technology based on handwriting. It has a wide application prospect in the fields of finance, government affairs, justice and so on. In this paper, firstly, the original document image is segmented into smaller regional samples by using the sliding window method. Secondly, multiple pre-training models are used to extract the sample features, and multiple features of the samples are fused. Finally, the euclidean distance is used to express the degree of difference between samples. Experiments show that this method has a high recognition rate and has a certain application value.

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Yeleussinov, Arman, Yedilkhan Amirgaliyev, and Lyailya Cherikbayeva. "Improving OCR Accuracy for Kazakh Handwriting Recognition Using GAN Models." Applied Sciences 13, no. 9 (May 5, 2023): 5677. http://dx.doi.org/10.3390/app13095677.

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This paper aims to increase the accuracy of Kazakh handwriting text recognition (KHTR) using the generative adversarial network (GAN), where a handwriting word image generator and an image quality discriminator are constructed. In order to obtain a high-quality image of handwritten text, the multiple losses are intended to encourage the generator to learn the structural properties of the texts. In this case, the quality discriminator is trained on the basis of the relativistic loss function. Based on the proposed structure, the resulting document images not only preserve texture details but also generate different writer styles, which provides better OCR performance in public databases. With a self-created dataset, images of different types of handwriting styles were obtained, which will be used when training the network. The proposed approach allows for a character error rate (CER) of 11.15% and a word error rate (WER) of 25.65%.

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Tamanna Sachdeva, Et al. "A Novel Approach for Hand-written Digit Classification Using Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (November 5, 2023): 1627–35. http://dx.doi.org/10.17762/ijritcc.v11i9.9148.

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Humans' control over technology is at an all-time high, with applications ranging from visual object recognition to the dubbing of dialogue into silent films. Using algorithms for deep learning and machine learning. Similarly, the most crucial technologies are text line recognition fields of study and development, with an increasing number of potential outcomes. Handwriting recognition (HWR), also identified as Handwriting Text Acknowledgment, is the capacity of a computer to understand legibly handwritten input from bases such as paper documents, screens, and other devices. Evidently, we have performed handwritten digit recognition using MNIST datasets and SVM, Multi-Layer Perceptron (MLP), and CNN models in this research. Our primary purpose is to compare the accuracy and execution times of the aforementioned models to determine the optimal model for digit recognition.

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Zhang, Qian, Dong Wang, Run Zhao, Yinggang Yu, and JiaZhen Jing. "Write, Attend and Spell." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (September 9, 2021): 1–25. http://dx.doi.org/10.1145/3478100.

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Text entry on a smartwatch is challenging due to its small form factor. Handwriting recognition using the built-in sensors of the watch (motion sensors, microphones, etc.) provides an efficient and natural solution to deal with this issue. However, prior works mainly focus on individual letter recognition rather than word recognition. Therefore, they need users to pause between adjacent letters for segmentation, which is counter-intuitive and significantly decreases the input speed. In this paper, we present 'Write, Attend and Spell' (WriteAS), a word-level text-entry system which enables free-style handwriting recognition using the motion signals of the smartwatch. First, we design a multimodal convolutional neural network (CNN) to abstract motion features across modalities. After that, a stacked dilated convolutional network with an encoder-decoder network is applied to get around letter segmentation and output words in an end-to-end way. More importantly, we leverage a multi-task sequence learning method to enable handwriting recognition in a streaming way. We construct the first sequence-to-sequence handwriting dataset using smartwatch. WriteAS can yield 9.3% character error rate (CER) on 250 words for new users and 3.8% CER for words unseen in the training set. In addition, WriteAS can handle various writing conditions very well. Given the promising performance, we envision that WriteAS can be a fast and accurate input tool for smartwatch.

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Abdallah, Abdelrahman, Mohamed Hamada, and Daniyar Nurseitov. "Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text." Journal of Imaging 6, no. 12 (December 18, 2020): 141. http://dx.doi.org/10.3390/jimaging6120141.

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This article considers the task of handwritten text recognition using attention-based encoder–decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) and attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER), and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Our proposed model is the first work to handle handwriting recognition models in Kazakh and Russian languages. Our results confirm the importance of our proposed Attention-Gated-CNN-BGRU approach for training handwriting text recognition and indicate that it can lead to statistically significant improvements (p-value < 0.05) in the sensitivity (recall) over the tests dataset. The proposed method’s performance was evaluated using handwritten text databases of three languages: English, Russian, and Kazakh. It demonstrates better results on the Handwritten Kazakh and Russian (HKR) dataset than the other well-known models.

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Gupta, Monica, Alka Choudhary, and Jyotsna Parmar. "Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition." International Journal of Computer Vision and Image Processing 11, no. 4 (October 2021): 39–62. http://dx.doi.org/10.4018/ijcvip.2021100104.

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In today's era, data in digitalized form is needed for faster processing and performing of all tasks. The best way to digitalize the documents is by extracting the text from them. This work of text extraction can be performed by various text identification tasks such as scene text recognition, optical character recognition, handwriting recognition, and much more. This paper presents, reviews, and analyses recent research expansion in the area of optical character recognition and scene text recognition based on various existing models such as convolutional neural network, long short-term memory, cognitive reading for image processing, maximally stable extreme regions, stroke width transformation, and achieved remarkable results up to 90.34% of F-score with benchmark datasets such as ICDAR 2013, ICDAR 2019, IIIT5k. The researchers have done outstanding work in the text recognition field. Yet, improvement in text detection in low-quality image performance is required, as text identification should not be limited to the input quality of the image.

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Wang, Weilan, Zhengjiang Li, Zhengqi Cai, Xiaobao Lv, Caike Zhaxi, and Yuehui Han. "Online Tibetan Handwriting Recognition for Large Character Set on New Databases." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (September 2019): 1953003. http://dx.doi.org/10.1142/s0218001419530033.

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The online handwriting recognition of Tibetan characters is still in its infancy. For further research, an online handwriting database of large Tibetan character set was developed, and a recognition research was carried out on this database as a baseline result. The Northwest Minzu University Online Tibetan Handwriting Database (NMU-OLTHWDB) contains 7240 different types of characters, and the sample number in each type is 5000. The total number of samples is [Formula: see text]. The database covers Tibetan Character Collection, Information Technology Tibetan Coded Character set (Extension Set A), and Information Technology Tibetan Coded Character set (Extension Set B). The characters in the database are composed of 170 types of different components. We studied the online handwritten Tibetan recognition software also, and the character feature extraction, classifier training, and the statistics and analysis of the recognition results on the test set were mainly introduced. The character features included the direction attribute coefficients and spatial combination, and the feature matrix was compressed by Linear Discriminate Analysis (LDA). A quick classifier was designed by a modified quadratic discriminate function (QMQDF), and was trained with 4500 sets of samples. In the large character set, the recognition rates of top 1, top 3, top 5, and top 10 were 75.2%, 89.56%, 93.02%, and 95.96%, respectively. Moreover, an online handwriting recognition system for Tibetan large character set was designed with good performance.

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Pathak, Vasundhara, Shriyansh Sharma, and Tanishka Goel. "Optical Character Recognition for Image & Handwriting to Text Conversion." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2603–6. http://dx.doi.org/10.22214/ijraset.2022.42476.

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Abstract: This paper combines the functionality of Optical Character Recognition and speech synthesizer. The idea is to develop stoner friendly operation which performs image to text conversion. Objective The advantage of proposed system that overcomes the disadvantage of the prevailing system is that it supports multiple functionalities like editing and searching. It also adds benefit by providing heterogeneous characters recognition

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P V, Pearlsy, and Deepa Sankar. "Handwriting-Based Text Line Segmentation from Malayalam Documents." Applied Sciences 13, no. 17 (August 28, 2023): 9712. http://dx.doi.org/10.3390/app13179712.

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Optical character recognition systems for Malayalam handwritten documents have become an open research area. A major hindrance in this research is the unavailability of a benchmark database. Therefore, a new database of 402 Malayalam handwritten document images and ground truth images of 7535 text lines is developed for the implementation of the proposed technique. This paper proposes a technique for the extraction of text lines from handwritten documents in the Malayalam language, specifically based on the handwriting of the writer. Text lines are extracted based on horizontal and vertical projection values, the size of the handwritten characters, the height of the text lines and the curved nature of the Malayalam alphabet. The proposed technique is able to overcome incorrect segmentation due to the presence of characters written with spaces above or below other characters and the overlapping of lines because of ascenders and descenders. The performance of the proposed method for text line extraction is quantitatively evaluated using the MatchScore value metric and is found to be 85.507%. The recognition accuracy, detection rate and F-measure of the proposed method are found to be 99.39%, 85.5% and 91.92%, respectively. It is experimentally verified that the proposed method outperforms some of the existing language-independent text line extraction algorithms.

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AL-Saffar, Ahmed, Suryanti Awang, Wafaa AL-Saiagh, Ahmed Salih AL-Khaleefa, and Saad Adnan Abed. "A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN." Sensors 21, no. 21 (November 2, 2021): 7306. http://dx.doi.org/10.3390/s21217306.

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Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods.

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Al-Maadeed, Somaya. "Text-Dependent Writer Identification for Arabic Handwriting." Journal of Electrical and Computer Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/794106.

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This paper proposes a system for text-dependent writer identification based on Arabic handwriting. First, a database of words was assembled and used as a test base. Next, features vectors were extracted from writers' word images. Prior to the feature extraction process, normalization operations were applied to the word or text line under analysis. In this work, we studied the feature extraction and recognition operations of Arabic text on the identification rate of writers. Because there is no well-known database containing Arabic handwritten words for researchers to test, we have built a new database of offline Arabic handwriting text to be used by the writer identification research community. The database of Arabic handwritten words collected from 100 writers is intended to provide training and testing sets for Arabic writer identification research. We evaluated the performance of edge-based directional probability distributions as features, among other characteristics, in Arabic writer identification. Results suggest that longer Arabic words and phrases have higher impact on writer identification.

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El Moubtahij, Hicham, A. Halli, and Khalid Satori. "Arabic Handwriting Text Offline Recognition Using the HMM Toolkit (HTK)." International Review on Computers and Software (IRECOS) 9, no. 7 (July 31, 2014): 1214. http://dx.doi.org/10.15866/irecos.v9i7.2258.

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Dasgupta, Poorna Banerjee. "Human Behavioral Analysis Based on Handwriting Recognition and Text Processing." International Journal of Computer Trends and Technology 64, no. 1 (October 25, 2018): 1–4. http://dx.doi.org/10.14445/22312803/ijctt-v64p101.

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Modi, Rohan. "Transcript Anatomization with Multi-Linguistic and Speech Synthesis Features." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1755–58. http://dx.doi.org/10.22214/ijraset.2021.35371.

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Handwriting Detection is a process or potential of a computer program to collect and analyze comprehensible input that is written by hand from various types of media such as photographs, newspapers, paper reports etc. Handwritten Text Recognition is a sub-discipline of Pattern Recognition. Pattern Recognition is refers to the classification of datasets or objects into various categories or classes. Handwriting Recognition is the process of transforming a handwritten text in a specific language into its digitally expressible script represented by a set of icons known as letters or characters. Speech synthesis is the artificial production of human speech using Machine Learning based software and audio output based computer hardware. While there are many systems which convert normal language text in to speech, the aim of this paper is to study Optical Character Recognition with speech synthesis technology and to develop a cost effective user friendly image based offline text to speech conversion system using CRNN neural networks model and Hidden Markov Model. The automated interpretation of text that has been written by hand can be very useful in various instances where processing of great amounts of handwritten data is required, such as signature verification, analysis of various types of documents and recognition of amounts written on bank cheques by hand.

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Lyall, F. C., P. J. Clamp, and D. Hajioff. "Smartphone speech-to-text applications for communication with profoundly deaf patients." Journal of Laryngology & Otology 130, no. 1 (November 27, 2015): 104–6. http://dx.doi.org/10.1017/s0022215115003151.

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AbstractObjective:Visual communication aids, such as handwriting or typing, are often used to communicate with deaf patients in the clinic. This study aimed to establish the feasibility of communicating through smartphone speech recognition software compared with writing or typing.Method:Thirty doctors and medical students were timed writing, typing and dictating a standard set of six sentences appropriate for a post-operative consultation, and the results were assessed for accuracy and legibility.Results:The mean time for smartphone dictation (17.8 seconds, 95 per cent confidence interval = 17.0–18.7) was significantly faster than writing (59.2 seconds, 95 per cent confidence interval = 56.6–61.7) or typing (44 seconds, 95 per cent confidence interval = 41.0–47.1) (p< 0.001). Speech recognition was slightly less accurate, but accuracy increased with time spent dictating.Conclusion:Smartphone dictation is a feasible alternative to typing and handwriting. Slow speech may improve accuracy. Early clinical experience has been promising.

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Saloum, Said S., and Iván García-Magariño. "Algorithm based on normal coordinate vectors with 16 segments for the data fusion from hand-written Arabic text implemented with MATLAB." PeerJ Computer Science 7 (September 9, 2021): e705. http://dx.doi.org/10.7717/peerj-cs.705.

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Hand-written text recognition is useful for interpreting records in different fields such as healthcare, surgery and police in which professionals may avoid technical equipment and prefer writing notes on paper. In order to perform data fusion from different data sources, handwriting automatic recognition involves barriers such as different ways of writing letters and deformation due to many reasons. This work presents a novel handwriting recognition approach based on the application of coordinate vectors to find similarities in different kinds of deformations. In particular, it has been implemented using 16 segments in order to distinguish all the particularities in matching the new text considering a dataset with a machine-learning approach. The implementation of this approach with MATLAB shows promising results with accuracy of 92.8% for with ensemble and bagged trees, after analyzing 22 possible combinations of machine learning and processing techniques.

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Toiganbayeva, N. A., Zh Zhunussova, A. Provata, and G. A. Tyulepberdinova. "Recognition of offline handwritten texts in kazakh-russian based on deep learning models." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 90, no. 4 (December 15, 2023): 126–36. http://dx.doi.org/10.47533/2023.1606-146x.49.

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The digitized text of handwritten notes allows you to automate the business processes of many companies and facilitates the work of a person. The article discusses the recognition of written handwriting in Russian and Kazakh languages using in-depth learning models. Due to the fact that each person’s handwriting is unique and there is no way to create general samples of handwritten text, the offline text recognition report is considered more complex than the online recognition report. The work uses various approaches to deep learning in the development of models of handwriting recognition in the Kazakh-Russian languages related to Cyrillic graphics. An important element of deep learning success is the availability of data, calculations, software platforms, and runtime, which makes it easier to build neural network models and execute them for production. The concepts of deep learning, a competitive, dynamically developing industry that provides fast, quantitative and fair means of analyzing and comparing different approaches and methods of learning, were discussed. Popular deep learning models such as Abdallah, Bluche, Flor and PUIGCERVER were reviewed and the results of the experiments were analyzed based on histograms. The experiments were based on a large database of offline handwritten texts in the Kazakh language called the Kazakh Offline Handwritten Text Dataset (KOHTD). A Telegram bot was created specifically for collecting handwritten data in the Kazakh language. A tool for checking the information entered through this Telegram bot and correcting responses was developed on the basis of a neural network.

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