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Journal articles on the topic 'Handwritten characters'

Author: Grafiati

Published: 18 May 2024

Last updated: 31 July 2025

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1

Jehangir, Sardar, Sohail Khan, Sulaiman Khan, Shah Nazir, and Anwar Hussain. "Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis." January 2021 40, no. 1 (2021): 152–59. http://dx.doi.org/10.22581/muet1982.2101.14.

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This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten Pashto characters database. For experimental and simulation purposes a handwritten Pashto characters database is

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Goyal, Samta Jain, and Rajeev Goyal. "An Idea to Recognition of handwritten Characters using Genetic Algorithms." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 04 (2015): 1686–89. https://doi.org/10.5281/zenodo.14776380.

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Challenges in handwritten characters recognition is due to the variation and distortion of handwritten characters, since different people use different style and way of draw the same shape of the characters. This paper demonstrates the nature of handwritten characters, conversion of handwritten data into electronic data, and the neural network approach to make machine capable of recognizing hand written characters. This motivates the use of Genetic Algorithms for the problem. In order to prove this, we made a pool of images of characters. We converted them to graphs. The graph of every charact

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Tiptur Parashivamurthy, Supreetha Patel, and Dr Sannangi Viswaradhya Rajashekararadhya. "An Efficient Kannada Handwritten Character Recognition Framework with Serial Dilated Cascade Network for Kannada Scripts." Advances in Artificial Intelligence and Machine Learning 04, no. 03 (2024): 2499–516. http://dx.doi.org/10.54364/aaiml.2024.43146.

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The most significant problem present in the digitized world is handwritten character recognition and identification because it is helpful in various applications. The manual work needed for changing the handwritten character document into machine-readable texts is highly reduced by using the automatic identification approaches. Due to the factors of high variance in the writing styles beyond the globe, handwritten text size and low quality of handwritten text rather than printed text make handwritten character recognition to be very complex. The Kannada language has originated over the past 10

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Zhu, Cheng Hui, Wen Jun Xu, Jian Ping Wang, and Xiao Bing Xu. "Research on a Characteristic Extraction Algorithm Based on Analog Space-Time Process for Off-Line Handwritten Chinese Characters." Advanced Materials Research 433-440 (January 2012): 3649–55. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3649.

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On the absence of space-time information, it is difficult to extract the character stroke feature from the off-line handwritten Chinese character image. A feature extraction algorithm is proposed based on analog space-time process by the process neural network. The handwritten Chinese character image is transformed into geometric shape by different types, different numbers, different locations, different orders and different structures of Chinese character strokes. By extracting fault-tolerant features of the five kinds of the off-line handwritten Chinese characters, the data-knowledge table o

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Gouda, Subhashree, and Prof Dr Sasmita Lenka. "Pattern Recognition Using Artificial Neural Network." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51342.

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Pattern recognition using Artificial Neural Networks (ANNs) for handwritten character recognition is a complex task that involves training ANNs to learn patterns and relationships between handwritten characters and their corresponding classes. The process begins with data collection, where a dataset of handwritten characters is gathered and preprocessed to enhance quality and consistency. Relevant features are then extracted from the preprocessed data, which are used to train an ANN model using a suitable algorithm such as backpropagation. The ANN architecture typically consists of an input la

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M, Ameen Chhajro, Khan Hadeeb, Khan Farrukh, Kumar Kamlesh, Ali Wagan Asif, and Solangi Sadaf. "Handwritten Urdu character recognition via images using different machine learning and deep learning techniques." Indian Journal of Science and Technology 13, no. 17 (2020): 1746–54. https://doi.org/10.17485/IJST/v13i17.113.

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Abstract <strong>Objectives:</strong> This research presents a model for Urdu Handwritten Character Recognition via images using various Machine Learning and Deep Learning Techniques. The main objective of this research is to provide comparative study on Urdu Handwritten Characters from images dataset. <strong>Methods/Statistical analysis:</strong> In this research paper, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithm, Multi-Layer Perceptron (MLP), Concurrent Neural Network (CNN), Recurrent Neural Network (RNN) and Random Forest Algorithm (RF) have been implem

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Khan, Sulaiman, Habib Ullah Khan, and Shah Nazir. "Offline Pashto Characters Dataset for OCR Systems." Security and Communication Networks 2021 (July 27, 2021): 1–7. http://dx.doi.org/10.1155/2021/3543816.

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In computer vision and artificial intelligence, text recognition and analysis based on images play a key role in the text retrieving process. Enabling a machine learning technique to recognize handwritten characters of a specific language requires a standard dataset. Acceptable handwritten character datasets are available in many languages including English, Arabic, and many more. However, the lack of datasets for handwritten Pashto characters hinders the application of a suitable machine learning algorithm for recognizing useful insights. In order to address this issue, this study presents th

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Mujadded, Al Rabbani Alif. "State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture." State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture 9, no. 1 (2024): 11. https://doi.org/10.5281/zenodo.10538255.

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Bangla Handwritten Character Recognition (HCR) remains a persistent challenge within the domain of Optical Character Recognition (OCR) systems. Despite extensive research efforts spanning several decades, achieving satisfactory success in this field has proven to be complicated. Bangla, being one of the most widely spoken languages worldwide, consists of 50 primary characters, including 11 vowels and 39 consonants. Unlike Latin languages, Bangla characters exhibit complex patterns, diverse sizes, significant variations, intricate letter shapes, and intricate edges. These characteristics furthe

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Amulya, K., Lakshmi Reddy, M. Chandara Kumar, and Rachana D. "A Survey on Digitization of Handwritten Notes in Kannada." International Journal of Innovative Technology and Exploring Engineering 12, no. 1 (2022): 6–11. http://dx.doi.org/10.35940/ijitee.a9350.1212122.

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Recognition of handwritten text is still an unresolved research problem in the field of optical character recognition. This article suggests an efficient method for creating handwritten text recognition systems. This is a challenging subject that has received a lot of attention recently. A discipline known as optical character recognition makes it possible to convert many kinds of texts or photos into editable, searchable, and analyzable data. Researchers have been using artificial intelligence and machine learning methods to automatically evaluate printed and handwritten documents during the

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K, Amulya, Reddy Lakshmi, Chandara Kumar M, and D. Rachana. "A Survey on Digitization of Handwritten Notes in Kannada." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 12, no. 1 (2022): 6–11. https://doi.org/10.35940/ijitee.A9350.1212122.

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<strong>Abstract: </strong>Recognition of handwritten text is still an unresolved research problem in the field of optical character recognition. This article suggests an efficient method for creating handwritten text recognition systems. This is a challenging subject that has received a lot of attention recently. A discipline known as optical character recognition makes it possible to convert many kinds of texts or photos into editable, searchable, and analyzable data. Researchers have been using artificial intelligence and machine learning methods to automatically evaluate printed and handwr

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Khan, Majid A., Nazeeruddin Mohammad, Ghassen Ben Brahim, Abul Bashar, and Ghazanfar Latif. "Writer verification of partially damaged handwritten Arabic documents based on individual character shapes." PeerJ Computer Science 8 (April 20, 2022): e955. http://dx.doi.org/10.7717/peerj-cs.955.

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Author verification of handwritten text is required in several application domains and has drawn a lot of attention within the research community due to its importance. Though, several approaches have been proposed for the text-independent writer verification of handwritten text, none of these have addressed the problem domain where author verification is sought based on partially-damaged handwritten documents (e.g., during forensic analysis). In this paper, we propose an approach for offline text-independent writer verification of handwritten Arabic text based on individual character shapes (

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Wijaya, Aditya Surya, Nurul Chamidah, and Mayanda Mega Santoni. "Pengenalan Karakter Tulisan Tangan Dengan K-Support Vector Nearest Neighbor." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 9, no. 1 (2019): 33. http://dx.doi.org/10.22146/ijeis.38729.

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Handwritten characters are difficult to be recognized by machine because people had various own writing style. This research recognizes handwritten character pattern of numbers and alphabet using K-Nearest Neighbour (KNN) algorithm. Handwritten recognition process is worked by preprocessing handwritten image, segmentation to obtain separate single characters, feature extraction, and classification. Features extraction is done by utilizing Zone method that will be used for classification by splitting this features data to training data and testing data. Training data from extracted features red

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Revathi, Buddaraju, M. V. D. Prasad, and Naveen Kishore Gattim. "Computationally efficient handwritten Telugu text recognition." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 1618–26. https://doi.org/10.11591/ijeecs.v34.i3.pp1618-1626.

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Optical character recognition (OCR) for regional languages is difficult due to their complex orthographic structure, lack of dataset resources, a greater number of characters and similarity in structure between characters. Telugu is popular language in states of Andhra and Telangana. Telugu exhibits distinct separation between characters within a word, making a character-level dataset sufficient. With a smaller dataset, we can effectively recognize more words. However, challenges arise during the training of compound characters, which are combinations of vowels and consonants. These are consid

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MALIK, LATESH, and P. S. DESHPANDE. "RECOGNITION OF HANDWRITTEN DEVANAGARI SCRIPT." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (2010): 809–22. http://dx.doi.org/10.1142/s0218001410008123.

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Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper, we propose a float fill algorithm for segmentation of unconstrained Devanagari text into words. Here, a text image is directly segmented into individual words. Rectangular boundaries are drawn around the words and horizontal lines are detected with template matching. A mask is designed for detecting the horizontal line and is applied to each word from left to right and top to bottom of the document. Header lines are removed for character se

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Revathi, Buddaraju, M. V. D. Prasad, and Naveen Kishore Gattim. "Computationally efficient handwritten Telugu text recognition." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 1618. http://dx.doi.org/10.11591/ijeecs.v34.i3.pp1618-1626.

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<p>Optical character recognition (OCR) for regional languages is difficult due to their complex orthographic structure, lack of dataset resources, a greater number of characters and similarity in structure between characters. Telugu is popular language in states of Andhra and Telangana. Telugu exhibits distinct separation between characters within a word, making a character-level dataset sufficient. With a smaller dataset, we can effectively recognize more words. However, challenges arise during the training of compound characters, which are combinations of vowels and consonants. These a

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Zhang, Yan, and Liumei Zhang. "SGooTY: A Scheme Combining the GoogLeNet-Tiny and YOLOv5-CBAM Models for Nüshu Recognition." Electronics 12, no. 13 (2023): 2819. http://dx.doi.org/10.3390/electronics12132819.

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With the development of society, the intangible cultural heritage of Chinese Nüshu is in danger of extinction. To promote the research and popularization of traditional Chinese culture, we use deep learning to automatically detect and recognize handwritten Nüshu characters. To address difficulties such as the creation of a Nüshu character dataset, uneven samples, and difficulties in character recognition, we first build a large-scale handwritten Nüshu character dataset, HWNS2023, by using various data augmentation methods. This dataset contains 5500 Nüshu images and 1364 labeled character samp

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Zhao, Yuliang, Xinyue Zhang, Boya Fu, et al. "Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities." Applied Sciences 12, no. 17 (2022): 8521. http://dx.doi.org/10.3390/app12178521.

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To accurately recognize ordinary handwritten Chinese characters, it is necessary to recognize the normative level of these characters. This study proposes methods to quantitatively evaluate and recognize these characters based on their similarities. Three different types of similarities, including correlation coefficient, pixel coincidence degree, and cosine similarity, are calculated between handwritten and printed Song typeface Chinese characters. Eight features are derived from the similarities and used to verify the evaluation performance and an artificial neural network is used to recogni

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VINITA, PATIL. "Design of High Efficient and High Recognition Rate for Real Time Handwritten Recognition using HMM and ANN Classification." International Journal of Control Theory and Applications 10 (May 3, 2023): 389–401. https://doi.org/10.5281/zenodo.7890708.

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Handwritten character recognition has been an active and challenging research problem. Most of the traditional methods have two challenges, due to the large variations of characters and the dependency relationship between characters. First, in real applications, words may be written cursively, so it is hard to identify the words automatically. Even if the words are neat, different people may write the same words in different styles. Since there are large shape variations in human handwriting, recognition accuracy of handwritten words is very difficult. Finally, dependency relationship between

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Tirapathi Reddy B. "Handwritten Character Recognition System." Journal of Electrical Systems 20, no. 3 (2024): 1465–75. http://dx.doi.org/10.52783/jes.3553.

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Digitizing handwritten documents and enabling efficient information processing and retrieval require systems that can recognize handwritten characters. This research offers a unique approach for handwritten character detection using state-of-the-art machine learning algorithms. The proposed technique automatically extracts discriminative features from photos of handwritten characters using convolutional neural networks (CNNs). These attributes are then used by a classifier to determine which characters are related. The dataset used for training and assessment is made up of a large collection o

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Santosh, Acharya, Dhungel Shashank, and Kr. Jha Ashish. "Nepali Handwritten Character Recognition System." Advancement in Image Processing and Pattern Recognition 5, no. 3 (2022): 1–6. https://doi.org/10.5281/zenodo.7472398.

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Even if the technological and digital world is expanding more quickly, there are still many things that are lacking. What a wonderful thing it would be to be able to trust machines to scan any handwritten characters into digital representation. The method for doing this is called optical character recognition (OCR), but there is still much room for improvement. Although there has been work done on it, the technique developed for one language cannot be applied to another due to language variations. Nepali is not a language that is frequently used online. Perhaps this is why there are fewer OCR

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El Ibrahimi, Aissam, Abdellah Elzaar, Nabil El Akchioui, Nabil Benaya, and Abderrahim El Allati. "Advancements in CNN Architectures for Offline Handwritten Arabic Character Recognition." E3S Web of Conferences 601 (2025): 00015. https://doi.org/10.1051/e3sconf/202560100015.

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Analyzing and classifying images of Arabic handwritten characters is crucial for text understanding and interpretation from image data. The recognition of handwritten Arabic characters not only preserves the integrity of the Arabic language but also enhances computer vision applications tailored for Arabic script. Existing literature often proposes complex architectures, which can hinder real-time prediction speed and accuracy. In this paper, we propose a novel Deep Learning architecture based on Convolutional Neural Networks (CNNs) for accurate classification of Arabic handwritten characters.

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Somashekar, Thatikonda. "A Survey on Handwritten Character Recognition using Machine Learning Technique." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 1019–24. http://dx.doi.org/10.51201/jusst/21/05304.

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Due to its broad range of applications, handwritten character recognition is widespread. Processing application forms, digitizing ancient articles, processing postal addresses, processing bank checks, and many other handwritten character processing fields are increasing in popularity. Since the last three decades, handwritten characters have drawn the attention of researchers. For successful recognition, several methods have been suggested. This paper presents a comprehensive overview of handwritten character recognition using a neural network as a machine learning tool.

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Wadaskar, Ghanshyam, Vipin Bopanwar, Prayojita Urade, Shravani Upganlawar, and Prof Rakhi Shende. "Handwritten Character Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 508–11. http://dx.doi.org/10.22214/ijraset.2023.57366.

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Abstract: Handwritten character recognition is a fascinating topic in the field of artificial intelligence. It involves developing algorithms and models that can analyze and interpret handwritten characters, such as letters, numbers, or symbols. The goal is to accurately convert handwritten text into digital form, making it easier to process and understand. It's a complex task, but with advancements in machine learning and deep learning techniques, significant progress has been made in this area.Handwritten character recognition is all about teaching computers to understand and interpret handw

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Kanmani, Dr S., B. Sujitha, K. Subalakshmi, S. Umamaheswari, and Karimreddy Punya Sai Teja Reddy. "Off-Line and Online Handwritten Character Recognition Using RNN-GRU Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2518–26. http://dx.doi.org/10.22214/ijraset.2023.50184.

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Abstract: Recognizing handwritten characters is an extremely difficult task in the domains of pattern recognition and computer vision. It involves the use of a process that enables computers to identify and convert handwritten or printed characters, such as letters and numbers, into a digital format that is usable by the computer. Currently, the RNN-CNN hybrid algorithm is employed to predict handwritten text in images with an accuracy rate of 91.5%. However, the existing system can only recognize characters and words character-by-character and word-by-word. The proposed system aims to address

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Teja, K. Sai. "Hindi-Handwritten-Character- Recognition using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 369–73. http://dx.doi.org/10.22214/ijraset.2023.54606.

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Abstract: Hindi-Handwritten-Character- Recognition is animportant problem in the field of machine learning andcomputer vision. With the increasing digitization of India, there is a growing need to develop accurate and efficient algorithms for recognizing handwritten Hindi characters, which can be used in a variety of applications such as document analysis, postal automation, and data entry. In recent years, deep learning has emerged as a powerful tool for solving complex recognition problems. In this work, we propose a deep learning-based approach to the Hindi-Handwritten Character-Recognition

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Mahto, Manoj Kumar, Karamjit Bhatia, and Rajendra Kumar Sharma. "Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 2 (2022): 69–82. http://dx.doi.org/10.5565/rev/elcvia.1282.

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Over the last few years, several researchers have worked on handwritten character recognition and have proposed various techniques to improve the performance of Indic and non-Indic scripts recognition. Here, a Deep Convolutional Neural Network has been proposed that learns deep features for offline Gurmukhi handwritten character and numeral recognition (HCNR). The proposed network works efficiently for training as well as testing and exhibits a good recognition performance. Two primary datasets comprising of offline handwritten Gurmukhi characters and Gurmukhi numerals have been employed in th

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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.

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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

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Buddaraju, Revathi B. N. V. Narasimha Raju Boddu L. V. Siva Rama Krishna Ajay Dilip Kumar Marapatla S. Suryanarayanaraju. "Efficient deep learning models for Telugu handwritten text recognition." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1564–72. https://doi.org/10.11591/ijeecs.v36.i3.pp1564-1572.

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Optical character recognition (OCR) technology is indispensable for converting and analyzing text from various sources into a format that is editable and searchable. Telugu handwriting presents notable challenges due to the resemblance of characters, the extensive character set, and the need to segment overlapping characters. To segment the overlapping characters, we assess the width of small characters within a word and segment the overlapping characters accordingly. This method is well suited for the segmentation of overlapping compound characters. To address the recognition of similar chara

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Alwaqfi, Yazan, Mumtazimah Mohamad, and Ahmad Al-Taani. "Generative Adversarial Network for an Improved Arabic Handwritten Characters Recognition." International Journal of Advances in Soft Computing and its Applications 14, no. 1 (2022): 177–95. http://dx.doi.org/10.15849/ijasca.220328.12.

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Abstract Currently, Arabic character recognition remains one of the most complicated challenges in image processing and character identification. Many algorithms exist in neural networks, and one of the most interesting algorithms is called generative adversarial networks (GANs), where 2 neural networks fight against one another. A generative adversarial network has been successfully implemented in unsupervised learning and it led to outstanding achievements. Furthermore, this discriminator is used as a classifier in most generative adversarial networks by employing the binary sigmoid cross-en

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Yadav, Bharati, Ajay Indian, and Gaurav Meena. "HDevChaRNet: A deep learning-based model for recognizing offline handwritten devanagari characters." Journal of Autonomous Intelligence 6, no. 2 (2023): 679. http://dx.doi.org/10.32629/jai.v6i2.679.

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<p>Optical character recognition (OCR) converts text images into machine-readable text. Due to the non-availability of several standard datasets of Devanagari characters, researchers have used many techniques for developing an OCR system with varying recognition rates using their own created datasets. The main objective of our proposed study is to improve the recognition rate by analyzing the effect of using batch normalization (BN) instead of dropout in convolutional neural network (CNN) architecture. So, a CNN-based model HDevChaRNet (Handwritten Devanagari Character Recognition Networ

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Wan Mohd Taufek, Wan Nurul Syafawani, Helmi Mohd Hadi Pritam, Nur Syuhaila Mat Desa, Dzulkiflee Ismail, and Naji Arafat Mahat. "Geometric Morphometric and Pattern Discrimination of Handwritten Numeral Characters Based on Local Ethnicities and Native Linguistic Disparities in Malaysia for Forensic Applications." Malaysian Journal of Fundamental and Applied Sciences 20, no. 5 (2024): 1068–82. http://dx.doi.org/10.11113/mjfas.v20n5.3639.

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Handwriting can be influenced by factors like culture and native language disparities; however, the specific research to discriminate handwritten numeral characters for forensic questioned document investigation involving the diverse ethnicities (Malay, Chinese and Indian) in Malaysian population remains unreported. Despite its application in forensic anthropology, utilization of the landmark based analysis using the Geometric Morphometric (GMM) technique for examining handwritten numeral character specimens appears sparse. Therefore, this present research that attempted to discriminate ethnic

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Revathi, Buddaraju, B. N. V. Narasimha Raju, Boddu L. V. Siva Rama Krishna, Ajay Dilip Kumar Marapatla, and S. Suryanarayanaraju Saripalle. "Efficient deep learning models for Telugu handwritten text recognition." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1564. http://dx.doi.org/10.11591/ijeecs.v36.i3.pp1564-1572.

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<p>Optical character recognition (OCR) technology is indispensable for converting and analyzing text from various sources into a format that is editable and searchable. Telugu handwriting presents notable challenges due to the resemblance of characters, the extensive character set, and the need to segment overlapping characters. To segment the overlapping characters, we assess the width of small characters within a word and segment the overlapping characters accordingly. This method is well suited for the segmentation of overlapping compound characters. To address the recognition of simi

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Lin, Cheng-Jian, Yu-Cheng Liu, and Chin-Ling Lee. "Automatic Receipt Recognition System Based on Artificial Intelligence Technology." Applied Sciences 12, no. 2 (2022): 853. http://dx.doi.org/10.3390/app12020853.

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In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics: printed and handwritten characters. Images of receipts with these characters are preprocessed separately. For handwritten characters, template matching and the fixed features of the receipts are used for text positioning, and projection is applied for character segmentation. Finally, a convolutional neural network is used for character recogni

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Huang, Juanjuan, Ihtisham Ul Haq, Chaolan Dai, Sulaiman Khan, Shah Nazir, and Muhammad Imtiaz. "Isolated Handwritten Pashto Character Recognition Using a K-NN Classification Tool based on Zoning and HOG Feature Extraction Techniques." Complexity 2021 (March 24, 2021): 1–8. http://dx.doi.org/10.1155/2021/5558373.

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Handwritten text recognition is considered as the most challenging task for the research community due to slight change in different characters’ shape in handwritten documents. The unavailability of a standard dataset makes it vaguer in nature for the researchers to work on. To address these problems, this paper presents an optical character recognition system for the recognition of offline Pashto characters. The problem of the unavailability of a standard handwritten Pashto characters database is addressed by developing a medium-sized database of offline Pashto characters. This database consi

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Suthar, Sanket B., and Amit R. Thakkar. "CNN-Based Optical Character Recognition for Isolated Printed Gujarati Characters and Handwritten Numerals." International Journal of Mathematical, Engineering and Management Sciences 7, no. 5 (2022): 643–55. http://dx.doi.org/10.33889/ijmems.2022.7.5.042.

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Optical character recognition (OCR) technologies have made significant progress in the field of language recognition. Gujarati is a more difficult language to recognize compared to other languages because of curves, close loops, the inclusion of modifiers, and the presence of joint characters. So great effort has been laid into the literature for Gujarati OCR. Recently deep learning-based CNN models are applied to develop OCR for different languages but Convolutional Neural Networks (CNN) models are not yet giving a satisfactory performance to recognize Gujarati characters. So, this paper prop

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Naidu, D. J. Samatha, and T. Mahammad Rafi. "HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS." International Journal of Computer Science and Mobile Computing 10, no. 8 (2021): 41–45. http://dx.doi.org/10.47760/ijcsmc.2021.v10i08.007.

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Handwritten character Recognition is one of the active area of research where deep neural networks are been utilized. Handwritten character Recognition is a challenging task because of many reasons. The Primary reason is different people have different styles of handwriting. The secondary reason is there are lot of characters like capital letters, small letters & special symbols. In existing were immense research going on the field of handwritten character recognition system has been design using fuzzy logic and created on VLSI(very large scale integrated)structure. To Recognize the tamil

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Devi, N. "Offline Handwritten Character Recognition using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1483–89. http://dx.doi.org/10.22214/ijraset.2021.37610.

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Abstract: This paper focuses on the task of recognizing handwritten Hindi characters using a Convolutional Neural Network (CNN) based. The recognized characters can then be stored digitally in the computer or used for other purposes. The dataset used is obtained from the UC Irvine Machine Learning Repository which contains 92,000 images divided into training (80%) and test set (20%). It contains different forms of handwritten Devanagari characters written by different individuals which can be used to train and test handwritten text recognizers. It contains four CNN layers followed by three ful

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Liu, Zhenyu, and Jie Zhang. "Date Recognition of Handwritten Chinese Documents Based on Object Detection and Character Classification." Industry Science and Engineering 1, no. 8 (2024): 9–20. https://doi.org/10.62381/i245802.

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With the increasing degree of informatization in human society, paper documents are increasingly stored and processed in the form of electronic documents. People often want to extract key information such as dates in the process of digitizing paper documents. However, for text images with severe linking, extracting key information is very difficult. This paper proposes a date recognition framework for handwritten Chinese documents. First, YOLOv9 is trained to detect dates in images. Then single characters are segmented according to the pixels and character edges of the date area. And finally,

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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.

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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

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Lee, Hahn-Ming, Chin-Chou Lin, and Jyh-Ming Chen. "A Preclassification Method for Handwritten Chinese Character Recognition Via Fuzzy Rules and Seart Neural Net." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 06 (1998): 743–61. http://dx.doi.org/10.1142/s0218001498000427.

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In this paper, a method of character preclassification for handwritten Chinese character recognition is proposed. Since the number of Chinese characters is very large (at least 5401s for daily use), we employ two stages to reduce the candidates of an input character. In stage I, we extract the first set of primitive features from handwritten Chinese characters and use fuzzy rules to create four preclassification groups. The purpose in stage I is to reduce the candidates roughly. In stage II, we extract the second set of primitive features from handwritten Chinese characters and then use the Su

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Ahsan, Shahrukh, Shah Tarik Nawaz, Talha Bin Sarwar, M. Saef Ullah Miah, and Abhijit Bhowmik. "A machine learning approach for Bengali handwritten vowel character recognition." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 1143. http://dx.doi.org/10.11591/ijai.v11.i3.pp1143-1152.

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Recognition of handwritten characters is complex because of the different shapes and numbers of characters. Many handwritten character recognition strategies have been proposed for both English and other major dialects. Bengali is generally considered the fifth most spoken local language in the world. It is the official and most widely spoken language of Bangladesh and the second most widely spoken among the 22 posted dialects of India. To improve the recognition of handwritten Bengali characters, we developed a different approach in this study using face mapping. It is quite effective in dist

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Shahrukh, Ahsan, Tarik Nawaz Shah, Bin Sarwar Talha, Saef Ullah Miah M., and Bhowmik Abhijit. "A machine learning approach for Bengali handwritten vowel character recognition." International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 1143–52. https://doi.org/10.11591/ijai.v11i3.pp1143-1152.

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Recognition of handwritten characters is complex because of the different shapes and numbers of characters. Many handwritten character recognition strategies have been proposed for both English and other major dialects. Bengali is generally considered the fifth most spoken local language in the world. It is the official and most widely spo-ken language of Bangladesh and the second most widely spoken among the 22 posted dialects of India. To improve the recognition of handwritten Bengali characters, we developed a different approach in this study using face mapping. It is quite effective in dis

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Asraful, Md, Md Anwar Hossain, and Ebrahim Hossen. "Handwritten Bengali Alphabets, Compound Characters and Numerals Recognition Using CNN-based Approach." Annals of Emerging Technologies in Computing 7, no. 3 (2023): 60–77. http://dx.doi.org/10.33166/aetic.2023.03.003.

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Accurately classifying user-independent handwritten Bengali characters and numerals presents a formidable challenge in their recognition. This task becomes more complicated due to the inclusion of numerous complex-shaped compound characters and the fact that different authors employ diverse writing styles. Researchers have recently conducted significant researches using individual approaches to recognize handwritten Bangla digits, alphabets, and slightly compound characters. To address this, we propose a straightforward and lightweight convolutional neural network (CNN) framework to accurately

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Ning, Zihao. "Research on Handwritten Chinese Character Recognition Based on BP Neural Network." Modern Electronic Technology 6, no. 1 (2022): 12. http://dx.doi.org/10.26549/met.v6i1.11359.

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The application of pattern recognition technology enables us to solve various human-computer interaction problems that were difficult to solve before. Handwritten Chinese character recognition, as a hot research object in image pattern recognition, has many applications in people’s daily life, and more and more scholars are beginning to study off-line handwritten Chinese character recognition. This paper mainly studies the recognition of handwritten Chinese characters by BP (Back Propagation) neural network. Establish a handwritten Chinese character recognition model based on BP neural network

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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 (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 tra

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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.

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<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

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Khatri, Suman, and Irphan Ali. "Hindi Numeral Recognition using Neural Network." International Journal of Advance Research and Innovation 1, no. 3 (2013): 29–39. http://dx.doi.org/10.51976/ijari.131304.

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Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. The constant development of computer tools lead to the requirement of easier interface between the man and the computer. Handwritten character recognition may for instance be applied to Zip-Code recognition, automatic printed form acquisition, or cheques reading. The importance to these applications has led to intense research for several years in the field of off-line handwritten character recognition. „Hindi‟ the national language of In

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Amin, Muhammad Sadiq, Siddiqui Muhammad Yasir, and Hyunsik Ahn. "Recognition of Pashto Handwritten Characters Based on Deep Learning." Sensors 20, no. 20 (2020): 5884. http://dx.doi.org/10.3390/s20205884.

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Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in

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Iqbal, Muhammad, Muniba Humayun, Raheel Siddiqi, Christopher J. Harrison, and Muneeb Abid Malik. "Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network." Sukkur IBA Journal of Computing and Mathematical Sciences 8, no. 1 (2024): 1–17. http://dx.doi.org/10.30537/sjcms.v8i1.1374.

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Handwritten Character recognition falls under the domain of image classification that has been under research for years. The idea is to make the machine recognize handwritten human characters. The language focused in this research paper is English while using offline handwritten character recognition for identifying English characters. There are many publically available datasets from which EMNIST is the most challenging one. The main idea of this research paper is to propose a deep learning CNN method to help recognize English characters. This research paper proposes a deep learning convoluti

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Siddiqui, Sayma Shafeeque A. W., Rajashri G. Kanke, Ramnath M. Gaikwad, and Manasi R. Baheti. "Review on Isolated Urdu Character Recognition: Offline Handwritten Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 384–88. http://dx.doi.org/10.22214/ijraset.2023.55164.

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Abstract: This paper summarizes a system for recognizing isolated Urdu characters using advanced machine learning algorithms. The system analyzes visual features of Urdu characters, like strokes and curves, to train models such as CNN, SVM, ANN, and MLP. With a large dataset, the system can accurately predict unseen characters. It can be integrated into various applications for real-time character recognition tasks like OCR (Optical Character Recognition) and handwriting recognition. This literature survey explores research papers focused on character recognition in languages like Urdu, Arabic

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