Log in

Relevant bibliographies by topics / Handwritten character recognition / Journal articles

To see the other types of publications on this topic, follow the link: Handwritten character recognition.

Journal articles on the topic 'Handwritten character recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Handwritten character recognition.'

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

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

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

1

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

JYOTI, A.PATIL, and SANJAY R. PATIL DR. "OPTICAL HANDWRITTEN DEVNAGARI CHARACTER RECOGNITION USING ARTIFICIAL NEURAL NETWORK APPROACH." IJIERT - International Journal of Innovations in Engineering Research and Technology 5, no. 3 (2018): 67–71. https://doi.org/10.5281/zenodo.1454101.

Full text

Abstract:

<strong>Character recognitions play a wide role in the fast moving world with the growing technology,by providing more scope to perform research in OCR techniques. In the field of pattern recognition Devnagari handwritten character recognition is one of the challenging research area. Character recognition is defined as electronic translation of scanned images of handwritten or printed text into a machine encoded text. In this paper proposed an off line handwritten Devnagari character recognition technique with the use of feed forward neural network. For training the neural network a handwritte

APA, Harvard, Vancouver, ISO, and other styles

4

Firdous, Saniya. "Handwritten Character Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1409–28. http://dx.doi.org/10.22214/ijraset.2022.42114.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

11

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

12

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

13

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

14

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

15

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.

Full text

Abstract:

<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

APA, Harvard, Vancouver, ISO, and other styles

16

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

17

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.

Full text

Abstract:

<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

APA, Harvard, Vancouver, ISO, and other styles

18

N S, Aswin. "Malayalam Handwritten Words Recognition: A Review." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30057.

Full text

Abstract:

This review examines character segmentation and offers an elegant method for identifying and transforming handwritten Malayalam words from picture documents into text. Character touchings, different writing styles, and noisy, damaged scanned photos make it difficult to recognise handwritten text. Taking use of today's world of rich data and algorithmic developments, the system uses deep convolutional neural networks (CNNs) to address these challenges. The three steps of Malayalam handwritten word recognition are segmentation, recognition, and pre-processing. Making Malayalam character datasets

APA, Harvard, Vancouver, ISO, and other styles

19

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

20

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

21

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

22

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 (2022): 73–78. http://dx.doi.org/10.22214/ijraset.2022.46520.

Full text

Abstract:

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,

APA, Harvard, Vancouver, ISO, and other styles

23

Abhale, Poonam Bhanudas. "Handwritten English Alphabet Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 2134–39. http://dx.doi.org/10.22214/ijraset.2021.39703.

Full text

Abstract:

Abstract: Character recognition is a process by which a computer recognizes letters, figures, or symbols and turns them into a digital form that a computer can use. In moment’s terrain character recognition has gained a lot of attention in the field of pattern recognition. Handwritten character recognition is useful in cheque processing in banks, form recycling systems, and numerous further. Character recognition is one of the well- liked and grueling areas of exploration. In the unborn character recognition produce a paperless terrain. In this paper, we describe the detailed study of the bein

APA, Harvard, Vancouver, ISO, and other styles

24

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

25

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

26

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

27

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

28

Jbrail, Mohammed Widad, and Mehmet Emin Tenekeci. "Character Recognition of Arabic Handwritten Characters Using Deep Learning." Journal of Studies in Science and Engineering 2, no. 1 (2022): 32–40. http://dx.doi.org/10.53898/josse2022213.

Full text

Abstract:

Optical character recognition (OCR) is used to digitize texts in printed documents and camera images. The most basic step in the OCR process is character recognition. The Arabic language is more complex than other alphabets, as the cursive is written in cursive and the characters have different spellings. Our research has improved a character recognition model for Arabic texts with 28 different characters. Character recognition was performed using Convolutional Neural Network models, which are accepted as effective in image processing and recognition. Three different CNN models have been propo

APA, Harvard, Vancouver, ISO, and other styles

29

Neha*1, &. Deepti Ahlawat2. "HANDWRITTEN ALPHANUMERIC CHARACTER RECOGNITION AND COMPARISON OF CLASSIFICATION TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 1 (2018): 419–28. https://doi.org/10.5281/zenodo.1147604.

Full text

Abstract:

Several techniques have been proposed by many researchers for handwritten as well as printed character and numerals recognition. Recognition is the process of conversion of handwritten text into machine readable form. To achieve the best accuracy of any recognition system the selection of feature extraction and classification technique is important. The data about the character is collected by the features and accordingly classifiers classify the character uniquely. For handwritten characters there are drawbacks like it differs from one writer to another, even when same person writes same char

APA, Harvard, Vancouver, ISO, and other styles

30

Padmaja, Kannuru. "Devanagari Handwritten Character Recognition Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 102–5. http://dx.doi.org/10.22214/ijraset.2022.39744.

Full text

Abstract:

Abstract: In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the

APA, Harvard, Vancouver, ISO, and other styles

31

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

32

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

33

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

34

Wakahara, Toru. "Toward robust handwritten character recognition." Pattern Recognition Letters 14, no. 4 (1993): 345–54. http://dx.doi.org/10.1016/0167-8655(93)90100-r.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Soselia, Davit, Magda Tsintsadze, Levan Shugliashvili, Irakli Koberidze, Shota Amashukeli, and Sandro Jijavadze. "On Georgian Handwritten Character Recognition." IFAC-PapersOnLine 51, no. 30 (2018): 161–65. http://dx.doi.org/10.1016/j.ifacol.2018.11.279.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Li, Ling Hua, Shou Fang Mi, and Heng Bo Zhang. "Template-Based Handwritten Numeric Character Recognition." Advanced Materials Research 586 (November 2012): 384–88. http://dx.doi.org/10.4028/www.scientific.net/amr.586.384.

Full text

Abstract:

This paper describes a stroke-based handwriting analysis method in classifying handwritten Numeric characters by using a template-based approach. Writing strokes are variable from time to time, even when the writing character is same and comes from the same user. Writing strokes include the properties such as the number of the strokes, the shapes and sizes of them and the writing order and the writing speed. We describe here a template-based system using the properties of writing strokes for the recognition of online handwritten numeric characters. Experimental results show that within the 150

APA, Harvard, Vancouver, ISO, and other styles

37

Ali, Aree, and Bayan Omer. "Invarianceness for Character Recognition Using Geo-Discretization Features." Computer and Information Science 9, no. 2 (2016): 1. http://dx.doi.org/10.5539/cis.v9n2p1.

Full text

Abstract:

<span style="font-size: 10pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Recognition rate of characters in the handwritten is still a big challenge for the research because of a shape variation, scale and format in a given handwritten character. A more complicated handwritten character recognition system needs a better feature extraction technique that deal with such variation of hand writing. In other hand, to obtai

APA, Harvard, Vancouver, ISO, and other styles

38

Rasika, R. Janrao *. Mr. D. D. Dighe. "HANDWRITTEN ENGLISH CHARACTER RECOGNITION USING LVQ AND KNN." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 8 (2016): 904–12. https://doi.org/10.5281/zenodo.60830.

Full text

Abstract:

A Handwritten character recognition (HCR) is an important task of detecting and recognizing in characters from the input digital image and convert it to other equivalent machine editable form. It gives high growth in image processing and pattern recognition. It has big challenges in data interpretation from language identification, bank cheques and conversion of any handwritten document into structural text form. Handwritten character recognition system uses a soft computing method like neural network, having area of research for long time with multiple theories and developed algorithm. Featur

APA, Harvard, Vancouver, ISO, and other styles

39

Deng, Peng, and Guiying Yang. "A Review of Research on Handwritten Chinese Character Recognition with Multi-Feature Fusion." Journal of Electronic Research and Application 8, no. 5 (2024): 109–17. http://dx.doi.org/10.26689/jera.v8i5.8401.

Full text

Abstract:

This paper analyzes the progress of handwritten Chinese character recognition technology, from two perspectives: traditional recognition methods and deep learning-based recognition methods. Firstly, the complexity of Chinese character recognition is pointed out, including its numerous categories, complex structure, and the problem of similar characters, especially the variability of handwritten Chinese characters. Subsequently, recognition methods based on feature optimization, model optimization, and fusion techniques are highlighted. The fusion studies between feature optimization and model

APA, Harvard, Vancouver, ISO, and other styles

40

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

41

Pornpanomchai, Chomtip, Verachad Wongsawangtham, Satheanpong Jeungudomporn, and Nannaphat Chatsumpun. "Thai Handwritten Character Recognition by Genetic Algorithm (THCRGA)." International Journal of Engineering and Technology 3, no. 2 (2011): 148–53. http://dx.doi.org/10.7763/ijet.2011.v3.214.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

OYENIRAN, OLUWASHINA O., JOSHUA O. OYENIYI, LAWRENCE O. OMOTOSHO, and IBRAHIM K. OGUNDOYIN. "DEVELOPMENT OF AN IMPROVED DATABASE FOR YORUBA HANDWRITTEN CHARACTER." Journal of Engineering Studies and Research 27, no. 4 (2021): 84–89. http://dx.doi.org/10.29081/jesr.v27i4.302.

Full text

Abstract:

For improved human comprehension and autonomous machine perception, optical character recognition has been saddled with the task of translating printed or hand-written materials into digital text files. Many works have been proposed and implemented in the computerization of different human languages in the global community, but microscopic attempts have also been made to place Yoruba Handwritten Character on the board of Optical Character Recognition. This study developed a novel available dataset for research on offline Yoruba handwritten character recognition so as to fill the gaps in the ex

APA, Harvard, Vancouver, ISO, and other styles

43

Kumar, K. Sathish, M. Shashivardhan Reddy, D. Hemanth Kumar, D. Shiva Kumar, N. Shiva, and Dr D. Thiyagarajan. "Hindi-Handwritten-Character-Recognition using Deep learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5252–55. http://dx.doi.org/10.22214/ijraset.2024.62768.

Full text

Abstract:

Abstract: Recognizing handwritten Hindi characters poses a significant challenge in the realms of machine learning and computer vision, particularly in the context of India's accelerating digitization. To address this, accurate and efficient algorithms are imperative for applications ranging from document analysis to postal automation and data entry. Leveraging the advancements in deep learning, we propose a novel approach to Hindi Handwritten Character Recognition. Our method employs a combination of Convolutional Neural Networks (CNNs) to extract image features and Recurrent Neural Networks

APA, Harvard, Vancouver, ISO, and other styles

44

Shital, Bagal, Deore Vaishnavi, Adsare Samiksha, Thube Shreya, and Bhandakkar M.P. "HAND WRITTEN CHARACTER RECOGNITION USING DEEP NEURAL NETWORK." Journal of the Maharaja Sayajirao University of Baroda 59, no. 1 (I) (2025): 233–37. https://doi.org/10.5281/zenodo.15180414.

Full text

Abstract:

ABSTRACTHandwritten character recognition (HCR) is a challenging problem in the field of computer vision,with numerous applications in automated data entry, postal sorting, and document digitization. Thispaper presents an approach for handwritten character recognition using deep neural networks,specifically employing a Convolutional Neural Network (CNN) algorithm. The CNN model is trainedon a comprehensive dataset of handwritten characters, and it automatically learns and extracts relevantfeatures from the input images. Through various layers of convolution, pooling, and fully connectednetwork

APA, Harvard, Vancouver, ISO, and other styles

45

Attigeri, Savitha. "Neural Network based Handwritten Character Recognition system." International Journal Of Engineering And Computer Science 7, no. 03 (2018): 23761–68. http://dx.doi.org/10.18535/ijecs/v7i3.18.

Full text

Abstract:

Handwritten character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network. Each character data set contains 26 alphabets. Fifty different character data sets are used for training the neural network. The traine

APA, Harvard, Vancouver, ISO, and other styles

46

R, Mr Venkatesh. "Handwritten Telugu Character Recognition & Signature Verification." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31955.

Full text

Abstract:

Behaviour reputation stands as one of the earliest applications in sample reputation. While spotting handwritten characters is an clean venture for humans, it is a formidable task for computer structures. Optical Character Recognition (OCR) is an crucial answer primarily based on optical systems, which enables automatic reputation of scanned and digitized characters This paper explores into optical man or woman popularity strategies in particular developed for handwriting Telugu within the characters. Telugu, a Dravidian language spoken especially in Andhra Pradesh and Telangana, India, offers

APA, Harvard, Vancouver, ISO, and other styles

47

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

48

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.

Full text

Abstract:

<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

APA, Harvard, Vancouver, ISO, and other styles

49

Abdul Naveed, Jakhro, Mudasar Ahmed Soomro, Leezna Saleem, and Muhammad Khalid Shaikh. "OHSCR: Benchmarks Dataset for Offline Handwritten Sindhi Character Recognition." Sir Syed University Research Journal of Engineering & Technology 14, no. 1 (2024): 55–61. http://dx.doi.org/10.33317/ssurj.618.

Full text

Abstract:

This research work presents a unique dataset for offline handwritten Sindhi character recognition. It has 7800 character images in total, divided into multiple categories by 150 writers of various ages, genders, and professional backgrounds. Each writer writes the 52 Sindhi characters in the designed form. With a high-quality scanner, all of the written samples were scanned. After that, all the handwritten Sindhi characters were cropped from the collected designed form, and the cropped images were saved in ‘.png’ format. For the benefit of the Sindhi research community, this work suggests an i

APA, Harvard, Vancouver, ISO, and other styles

50

Gassi, Sajad Ahmad, Ravinder Pal Singh, and Dr Monika Mehra. "Real Time Character Recognition using Convolution Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1156–62. http://dx.doi.org/10.22214/ijraset.2022.47540.

Full text

Abstract:

Abstract: Handwritten recognition of character (HCR) is a significant element in the current world and one of the focused fields in image processing and pattern recognition research. Handwritten recognition of character refers to the process of converting hand-written character into printed/word file character that in many applications may greatly enhance the interaction of man and machine. The styles, varied sizes and orientation angles of the current characters are tough to parse with large variances. In addition, it is hard to split cursive handwritten text as the edges cannot be clearly se

APA, Harvard, Vancouver, ISO, and other styles

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