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Journal articles on the topic 'Handwritten characters'
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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 (January 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 developed by collecting handwritten samples from the students of the university on A4 sized page. These collected samples are then scanned, stemmed and preprocessed to form a medium sized database that encompasses 14784 handwritten Pashto character images (336 distinguishing handwritten samples for each 44 characters in Pashto script). Furthermore, the Zernike moments are considered as a feature extractor tool for the proposed OCR system to extract features of each individual character. Linear Discriminant Analysis (LDA) is followed as a recognition tool for the proposed recognition system based on the calculated features map using Zernike moments. Applicability of the proposed system is tested by validating it with 10-fold cross-validation method and an overall accuracy of 63.71% is obtained for the handwritten Pashto isolated characters using the proposed OCR system.
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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 of features is constructed. The parameters of process neural networks are optimized by Particle Swarm optimization (PSO). The handwritten Chinese characters are used to carry out simulation experiment in SCUT-IRAC-HCCLIB. The experiment results show that the algorithm exhibits a strong ability of cognizing handwritten Chinese characters.
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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 the first handwritten Pashto characters image dataset (HPCID) for the scientific research work. This dataset consists of fourteen thousand, seven hundred, and eighty-four samples—336 samples for each of the 44 characters in the Pashto character dataset. Such samples of handwritten characters are collected on an A4-sized paper from different students of Pashto Department in University of Peshawar, Khyber Pakhtunkhwa, Pakistan. On total, 336 students and faculty members contributed in developing the proposed database accumulation phase. This dataset contains multisize, multifont, and multistyle characters and of varying structures.
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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 (August 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 separation. A new segment code features are extracted for each character. In this paper, we present the results of multiple classifier combination for offline handwritten Devanagari characters. The use of regular expressions in handwritten characters is a novel concept and they are defined in a manner so that they can become more robust to noise. We have achieved an accuracy of 94% for word level segmentation, 95% for coarse classification and 85% for fine classification of character recognition. On experimentation with a dataset of 5000 samples of characters, the overall recognition rate observed is 95% as we considered top five choice results. The proposed combined classifier can be applied to handwritten character recognition of any other language like English, Chinese, Arabic, etc. and can recognize the characters with same accuracy.18 For printed characters we have achieved accuracy of 100%, only by applying the regular expression classifier.17
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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 (December 30, 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 past ten years in order to digitize them. This review paper's goals are to present research directions and a summary of previous studies on character recognition in handwritten texts. Since different people have different handwriting styles, handwritten characters might be challenging to read. Our "Digitization of handwritten notes" research and effort is to categorize and identify characters in the south Indian language of Kannada. The characters are extracted from printed texts and pre-processed using NumPy and OpenCV before being fed through a CNN
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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 (within the Arabic alphabet). The proposed approach enables writer verification for partially damaged documents where certain handwritten characters can still be extracted from the damaged document. We also provide a mechanism to identify which Arabic characters are more effective during the writer verification process. We have collected a new dataset, Arabic Handwritten Alphabet, Words and Paragraphs Per User (AHAWP), for this purpose in a classroom setting with 82 different users. The dataset consists of 53,199 user-written isolated Arabic characters, 8,144 Arabic words, 10,780 characters extracted from these words. Convolutional neural network (CNN) based models are developed for verification of writers based on individual characters with an accuracy of 94% for isolated character shapes and 90% for extracted character shapes. Our proposed approach provided up to 95% writer verification accuracy for partially damaged documents.
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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 (April 30, 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 reduced by K-Support Vector Nearest Neighbor (K-SVNN) and for recognizing handwritten pattern from testing data, we used K-Nearest Neighbor (KNN). Testing result shows that reducing training data using K-SVNN able to improve handwritten character recognition accuracy.
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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 (June 1, 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 are considered as two or more characters based on associated vattus and dheerghams with the base character. To address this challenge, each compound character is encoded into a numerical value and used as input during training, with subsequent retrieval during recognition. The segmentation issue arises from overlapping characters caused by varying handwritten styles. For handling segmentation issues at the character level arising from handwritten styles, we have proposed an algorithm based on the language's features. To enhance word-level accuracy a dictionary-based model was devised. A neural network utilizing the inception module is employed for feature extraction at various scales, achieving word-level accuracy rates of 78% with fewer trainable parameters.</p>
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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 (June 26, 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 samples. Second, in this paper, we propose a two-stage scheme model combining GoogLeNet-tiny and YOLOv5-CBAM (SGooTY) for Nüshu recognition. In the first stage, five basic deep learning models including AlexNet, VGGNet16, GoogLeNet, MobileNetV3, and ResNet are trained and tested on the dataset, and the model structure is improved to enhance the accuracy of recognising handwritten Nüshu characters. In the second stage, we combine an object detection model to re-recognize misidentified handwritten Nüshu characters to ensure the accuracy of the overall system. Experimental results show that in the first stage, the improved model achieves the highest accuracy of 99.3% in recognising Nüshu characters, which significantly improves the recognition rate of handwritten Nüshu characters. After integrating the object recognition model, the overall recognition accuracy of the model reached 99.9%.
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Bhat, Mohammad Idrees, and B. Sharada. "Spectral Graph-based Features for Recognition of Handwritten Characters: A Case Study on Handwritten Devanagari Numerals." Journal of Intelligent Systems 29, no. 1 (July 21, 2018): 799–813. http://dx.doi.org/10.1515/jisys-2017-0448.
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Abstract Interpretation of different writing styles, unconstrained cursiveness and relationship between different primitive parts is an essential and challenging task for recognition of handwritten characters. As feature representation is inadequate, appropriate interpretation/description of handwritten characters seems to be a challenging task. Although existing research in handwritten characters is extensive, it still remains a challenge to get the effective representation of characters in feature space. In this paper, we make an attempt to circumvent these problems by proposing an approach that exploits the robust graph representation and spectral graph embedding concept to characterise and effectively represent handwritten characters, taking into account writing styles, cursiveness and relationships. For corroboration of the efficacy of the proposed method, extensive experiments were carried out on the standard handwritten numeral Computer Vision Pattern Recognition, Unit of Indian Statistical Institute Kolkata dataset. The experimental results demonstrate promising findings, which can be used in future studies.
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Zhao, Yuliang, Xinyue Zhang, Boya Fu, Zhikun Zhan, Hui Sun, Lianjiang Li, and Guanglie Zhang. "Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities." Applied Sciences 12, no. 17 (August 25, 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 recognize the character content. The results demonstrate that our proposed methods deliver satisfactory evaluation effectiveness and recognition accuracy (up to 98%~100%). This indicates that it is possible to improve the accuracy in recognition of ordinary handwritten Chinese characters by evaluating the normative level of these characters and standardizing writing actions in advance. Our study can offer some enlightenment for developing methods for the identification of handwritten Chinese characters used in transaction processing activities.
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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 (December 31, 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 handwritten text. It involves using advanced algorithms and machine learning techniques to analyze the shapes, lines, and curves of handwritten characters. The goal is to accurately recognize and convert them into digital form. This technology has various applications, such as digitizing handwritten documents, assisting in automatic form filling, and enabling handwriting-based input in devices like tablets and smartphones. It's a fascinating field that combines computer vision, pattern recognition, and artifical intelligence
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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 (June 18, 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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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 (April 30, 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 this limitation by enabling line-byline recognition and the conversion of handwritten text to OCR. To achieve this, the system utilizes the GRU algorithm to predict the next letter in incomplete words. Furthermore, the IAM dataset, consisting of 135,000 annotated sentences, is utilized to detect and rectify spelling errors in texts.
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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 (July 31, 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. Specifically, we use a convolutional neural network (CNN) to extract features from the input images, and are current neural network (RNN) to model the temporal dependencies in the sequence of characters. Our approach is evaluated on a benchmark dataset of handwritten Hindi characters, achieving state-of- the-art results in terms of recognition accuracy. We also demonstrate the effectiveness of our approach on real-worldapplications, such as recognizing handwritten postal addresses on envelopes. Overall, our work provides a promising solution to the problem of Hindi-Hand-written- Character-Recognition, which can havea significant impact on the digitization of India and other similar regions.
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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 (January 18, 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 the present work. The testing accuracies achieved using the proposed network is 98.5% for characters and 98.6% for numerals.
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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 (March 28, 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-entropy loss function. This research proposes employing sigmoid cross-entropy to recognize Arabic handwritten characters using multi-class GANs training algorithms. The proposed approach is evaluated on a dataset of 16800 Arabic handwritten characters. When compared to other approaches, the experimental results indicate that the multi-class GANs approach performed well in terms of recognizing Arabic handwritten characters as it is 99.7% accurate. Keywords: Generative Adversarial Networks (GANs), Arabic Characters, Optical Character Recognition, Convolutional Neural Networks (CNNs).
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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 (August 15, 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 Network) is proposed in this study for same to recognize offline handwritten Devanagari characters using a dataset named Devanagari handwritten character dataset (DHCD). DHCD comprises a total of 46 classes of characters, out of which 36 are consonants, and 10 are numerals. The proposed models based on convolutional neural network (CNN) with BN for recognizing the Devanagari characters showed an improved accuracy of 98.75%, 99.70%, and 99.17% for 36, 10, and 46 classes, respectively.</p>
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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 (January 14, 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 recognition. For printed characters, a modified You Only Look Once (version 4) model (YOLOv4-s) executes precise text positioning and character recognition. The proposed YOLOv4-s model reduces downsampling, thereby enhancing small-object recognition. Finally, the system produces recognition results in a tax declaration format, which can upload to a tax declaration system. Experimental results revealed that the recognition accuracy of the proposed system was 80.93% for handwritten characters. Moreover, the YOLOv4-s model had a 99.39% accuracy rate for printed characters; only 33 characters were misjudged. The recognition accuracy of the YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%. Therefore, the proposed ARRS can considerably improve the efficiency of tax declaration, reduce labor costs, and simplify operating procedures.
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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 consists of 11352 character images (258 samples for each 44 characters in a Pashto script). Enriched feature extraction techniques of histogram of oriented gradients and zoning-based density features are used for feature extraction of carved Pashto characters. K-nearest neighbors is considered as a classification tool for the proposed algorithm based on the proposed feature sets. A resultant accuracy of 80.34% is calculated for the histogram of oriented gradients, while for zoning-based density features, 76.42% is achieved using 10-fold cross validation.
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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 (October 1, 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 proposes a revolutionary Gujarati printed characters and numerals recognition CNN models. CNN-PGC (CNN for - Printed Gujarati Character) and CNN-HGC (CNN for - Handwritten Gujarati Character) are two optimally configured Convolutional Neural Networks (CNNs) presented in this research for printed Gujarati base characters and handwritten numbers, respectively. Concerning particular performance indicators, the suggested work's performance is evaluated and proven against that of other traditional models and with the latest baseline methods. Experimental analysis has been carried out on well-segmented newly generated Gujarati base characters and numerals dataset which includes 36 consonants, 13 vowels, and 10 handwritten numerals. Variation in the database is also taken into consideration during experiments like size, skew, noise blue, etc. Even in the presence of printing irregularities, writing irregularities, and degradations the proposed method achieves a 98.08% recognition rate for print characters and a 95.24 % recognition rate for handwritten numerals which is better than other existing models.
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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 (August 30, 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 characters they have use neural networks with the Kohonen self-organizing map(SOM) which is an unsupervised neural networks. In proposed system this project design a image segmentation based hand written character recognition system. The convolutional neural network is the current state of neural network which has wide application in fields like image, video recognition. The system easily identify or easily recognize text in English languages and letters, digits. By using Open cv for performing image processing and having tensor flow for training the neural network. To develop this concept proposing the innovative method for offline handwritten characters. detection using deep neural networks using python programming language.
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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 (August 31, 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 fully connected layers for recognition. Grayscale handwritten character images are used as input. Filters are applied on the images to extract different features at each layer. This is done by the Convolution operation. The two other main operations involved are Pooling and Flattening. The output of the CNN layers is fed to the fully connected layers. Finally, the chance or probability score of each character is determined and the character with the highest probability score is shown as the output. A recognition accuracy of 98.94% is obtained. Similar models exist for the purpose, but the proposed model achieved a better performance and accuracy than some of the earlier models. Keywords: Devanagari characters, Convolutional Neural Networks, Image Processing
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Sharma, Kartik, S. V. Jagadeesh Kona, Anshul Jangwal, Aarthy M, Prayline Rajabai C, and Deepika Rani Sona. "Handwritten Digits and Optical Characters Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (May 4, 2023): 20–24. http://dx.doi.org/10.17762/ijritcc.v11i4.6376.
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The process of transcribing a language represented in its spatial form of graphical characters into its symbolic representation is called handwriting recognition. Each script has a collection of characters or letters, often known as symbols, that all share the same fundamental shapes. Handwriting analysis aims to correctly identify input characters or images before being analysed by various automated process systems. Recent research in image processing demonstrates the significance of image content retrieval. Optical character recognition (OCR) systems can extract text from photographs and transform that text to ASCII text. OCR is beneficial and essential in many applications, such as information retrieval systems and digital libraries.
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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 (September 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 Supervised Extended ART (SEART) as the classifier to generate preclassification classes for each preclassification group created in stage I. Since the number of characters in each preclassification class is smaller than that in the whole character set, the problem becomes simpler. In order to evaluate the proposed preclassification system, we use 605 Chinese character categories in the textbooks of elementary school as our training and testing data. The database used is HCCRBASE (provided by CCL, ITRI, Taiwan). In samples 1–100, we select the even samples as the training set, and the odd samples as the testing set. The characters of the testing set can be distributed into correct preclassification classes at a rate of 98.11%.
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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 (September 1, 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 distinguishing different characters. The real highlight is that the recognition results are more efficient than expected with a simple machine learning technique. The proposed method uses the Python library Scikit-Learn, including NumPy, Pandas, Matplotlib, and support vector machine (SVM) classifier. The proposed model uses a dataset derived from the BanglaLekha isolated dataset for the training and testing part. The new approach shows positive results and looks promising. It showed accuracy up to 94% for a particular character and 91% on average for all characters.
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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 (July 1, 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 categorize handwritten Bangla simple characters, compound characters, and numerals. The suggested approach exhibits outperformance in terms of performance when compared too many previously developed procedures, with faster execution times and requiring fewer epochs. Furthermore, this model applies to more than three datasets. Our proposed CNN-based model has achieved impressive validation accuracies on three datasets. Specifically, for the BanglaLekha isolated dataset, which includes 84-character classes, the validation accuracy was 92.48%. On the Ekush dataset, which includes 60-character classes, the model achieved a validation accuracy of 97.24%, while on the customized dataset, which includes 50-character classes, the validation accuracy was 97.03%. Our model has demonstrated high accuracy and outperformed several prominent existing frameworks.
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Ning, Zihao. "Research on Handwritten Chinese Character Recognition Based on BP Neural Network." Modern Electronic Technology 6, no. 1 (June 23, 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, and then verify the accuracy and feasibility of the neural network through GUI (Graphical User Interface) model established by Matlab. This paper mainly includes the following aspects: Firstly, the preprocessing process of handwritten Chinese character recognition in this paper is analyzed. Among them, image preprocessing mainly includes six processes: graying, binarization, smoothing and denoising, character segmentation, histogram equalization and normalization. Secondly, through the comparative selection of feature extraction methods for handwritten Chinese characters, and through the comparative analysis of the results of three different feature extraction methods, the most suitable feature extraction method for this paper is found. Finally, it is the application of BP neural network in handwritten Chinese character recognition. The establishment, training process and parameter selection of BP neural network are described in detail. The simulation software platform chosen in this paper is Matlab, and the sample images are used to train BP neural network to verify the feasibility of Chinese character recognition. Design the GUI interface of human-computer interaction based on Matlab, show the process and results of handwritten Chinese character recognition, and analyze the experimental results.
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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 India (written in Devanagri script) is world‟s third most popular language after Chinese and English. Hindi handwritten character recognition has got lot of application in different fields like postal address reading, cheques reading electronically. Recognition of handwritten Hindi characters by computer machine is complicated task as compared to typed characters, which can be easily recognized by the computer. This paper presents a scheme to recognize Hindi number numeral with the help of neural network.
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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 (March 19, 2022): 32–40. http://dx.doi.org/10.53898/josse2022213.
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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 proposed. In the study, training and testing of the models were carried out using the Hijja data set. Among the proposed models, Model C with a 99.3% accuracy rate has obtained results that can compete with the studies in the literature.
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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 (October 17, 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 an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.
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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 (August 31, 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, Persian, and Sindhi, proposing various techniques like feature extraction, deep learning, and machine learning to enhance character recognition technology. The survey highlights specific studies with high accuracy and discusses recognition systems for Arabic characters as well.
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N S, Aswin. "Malayalam Handwritten Words Recognition: A Review." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 6, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30057.
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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 is the first stage, and then pre-processing to improve image quality comes next. Then, in order to maximise the system's capacity to precisely forecast Malayalam characters, a CNN model is built to extract relevant information. The last phase of the recognition process involves the system classifying the characters. This project is significant since it uses CNN filters to enhance feature recognition, which enhances the accuracy of Malayalam character prediction. Key Words: Deep Learning, Deep Convolution Neural Network (DCNN), Character recognition, Character segmentation,
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Premachandra, H. Waruna H., Maika Yamada, Chinthaka Premachandra, and Hiroharu Kawanaka. "Low-Computational-Cost Algorithm for Inclination Correction of Independent Handwritten Digits on Microcontrollers." Electronics 11, no. 7 (March 29, 2022): 1073. http://dx.doi.org/10.3390/electronics11071073.
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In recent years, the digitization of documents has progressed, and opportunities for handwritten document creation have decreased. However, handwritten notes are still taken for memorizing data, and automated digitalization is needed in some cases, such as making Excel sheets. When digitizing handwritten notes, manual input is required. Therefore, the automatic recognition and input of characters using a character recognition system is useful. However, if the characters are inclined, the recognition rate will be low. Therefore, we focus on the inclination correction problem of characters. The conventional method corrects the inclination and estimates the character line inclination. However, these methods do not work when characters exist in independent positions. Therefore, in this study, we propose a new method for estimating and correcting the tilt of independent handwritten digits by analyzing a circumscribed rectangle and other digital features. The proposed method is not based on an AI-based learning model or a complicated mathematical model. It is developed following a comparatively simple mathematical calculation that can be implemented on a microcontroller. Based on the results of the experiments using digits written in independent positions, the proposed method can correct the inclination with high accuracy. Furthermore, the proposed algorithm is low-computational cost and can be implemented in real-time on a microcontroller.
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Devaraj, Anjali Yogesh, Anup S. Jain, Omisha N, and Shobana TS. "Kannada Text Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 73–78. http://dx.doi.org/10.22214/ijraset.2022.46520.
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Abstract: The task of automatic handwriting recognition is critical. This can be a difficult subject, and it has gotten a lot of attention in recent years. In the realm of picture grouping, handwritten character recognition is a problem. Handwritten characters are difficult to decipher since various people have distinct handwriting styles. For decades, researchers have been focusing on character identification in Latin handwriting. Kannada has had fewer studies conducted on it. Our "Kannada Text Recognition" research and effort attempts to classify and recognize characters written in Kannada, a south Indian language. The characters are taken from written documents, preprocessed with numpy and OpenCV, and then run through a CNN.
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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.
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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 1500 numeric characters taken from 30 writers, the system got 97.84% recognition accuracy which is better than other systems shown by other literatures.
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Li, Kangying, Biligsaikhan Batjargal, and Akira Maeda. "A Prototypical Network-Based Approach for Low-Resource Font Typeface Feature Extraction and Utilization." Data 6, no. 12 (December 16, 2021): 134. http://dx.doi.org/10.3390/data6120134.
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This paper introduces a framework for retrieving low-resource font typeface databases by handwritten input. A new deep learning model structure based on metric learning is proposed to extract the features of a character typeface and predict the category of handwrittten input queries. Rather than using sufficient training data, we aim to utilize ancient character font typefaces with only one sample per category. Our research aims to achieve decent retrieval performances over more than 600 categories of handwritten characters automatically. We consider utilizing generic handcrafted features to train a model to help the voting classifier make the final prediction. The proposed method is implemented on the ‘Shirakawa font oracle bone script’ dataset as an isolated ancient-character-recognition system based on free ordering and connective strokes. We evaluate the proposed model on several standard character and symbol datasets. The experimental results showed that the proposed method provides good performance in extracting the features of symbols or characters’ font images necessary to perform further retrieval tasks. The demo system has been released, and it requires only one sample for each character to predict the user input. The extracted features have a better effect in finding the highest-ranked relevant item in retrieval tasks and can also be utilized in various technical frameworks for ancient character recognition and can be applied to educational application development.
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He, Rong. "Skeletonization of broken handwritten characters." Optical Engineering 39, no. 11 (November 1, 2000): 2882. http://dx.doi.org/10.1117/1.1315024.
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Srinivasa Chakravarthy, V., and Bhaskar Kompella. "The shape of handwritten characters." Pattern Recognition Letters 24, no. 12 (August 2003): 1901–13. http://dx.doi.org/10.1016/s0167-8655(03)00017-5.
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Srivastav, Ankita, and Neha Sahu. "Segmentation of Devanagari Handwritten Characters." International Journal of Computer Applications 142, no. 14 (May 18, 2016): 15–18. http://dx.doi.org/10.5120/ijca2016909994.
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Vaidehi K. and Manivannan R. "Automated Math Symbol Classification Using SVM." International Journal of e-Collaboration 18, no. 2 (March 1, 2022): 1–14. http://dx.doi.org/10.4018/ijec.304037.
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Handwritten character/symbol recognition is an important area of research in the present digital world. The solving of problems such as recognizing handwritten characters/symbols written in different styles can make the human job easier. Mathematical expression recognition using machines has become a subject of serious research. The main motivation for this work is both recognizing of the handwritten mathematical symbol, digits and characters which will be used for mathematical expression recognition. The system first identifies the contour in handwritten document segmentation and features extracted are given into SVM classifier for classification. GLCM and Zernike Moments are the two different feature extraction techniques used in this work. SVM with RBF kernel is used for classification. Zernike Moment features overperforms than GLCM. Zernike Moment achieves 97.89% accuracy and GLCM achieves 87.61% accuracy.
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Ali, Aree, and Bayan Omer. "Invarianceness for Character Recognition Using Geo-Discretization Features." Computer and Information Science 9, no. 2 (March 17, 2016): 1. http://dx.doi.org/10.5539/cis.v9n2p1.
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<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 obtain efficient and accurate recognition rely on off-line English handwriting character, the similarity in the character traits is an important issue to be differentiated in an off-line English handwriting to. In recognizing a character, character handwriting format could be implicitly analyzed to make the representation of the unique hidden features of the individual's character is allowable. Unique features can be used in recognizing characters which can be considerable when the similarity between two characters is high. However, the problem of the similarity in off-line English character handwritten was not taken into account thus, leaving a high possibility of degrading the similarity error for intra-class [same character] with the decrease of the similarity error for inter-class [different character]. Therefore, in order to achieve better performance, this paper proposes a discretization feature algorithm to reduce the similarity error for intra-class [same character]. The mean absolute error is used as a parameter to calculate the similarity between inter and/or intra class characters. Test results show that the identification rate give a better result with the proposed hybrid Geo-Discretization method.</span>
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R, Mr Venkatesh. "Handwritten Telugu Character Recognition & Signature Verification." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 28, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem31955.
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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 precise challenges because of its complex alphabet Basic parts of Telugu script together with "vattu" which stands for vowels and "gunitalu" which means that tone the complicated syllables add to the complexity. Combining OCR strategies with Harris corner popularity, the paper affords insights into the accuracy and efficiency of handwritten Telugu person reputation and the fidelity of handwriting This have a look at contributes to the development of character reputation in particular on in complex written languages which include Telugu and gives realistic explanations for handwriting verification processing. Key Words: Optical Character Recognition (OCR), Telugu, Vattu, Gunitalu, Harris corner detection, Handwritten Character Recognition, Signature Verification.
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HA, JIN-YOUNG, SE-CHANG OH, and JIN H. KIM. "RECOGNITION OF UNCONSTRAINED HANDWRITTEN ENGLISH WORDS WITH CHARACTER AND LIGATURE MODELING." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 03 (June 1995): 535–56. http://dx.doi.org/10.1142/s0218001495000511.
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In this paper, we proposed an approach for segmentation and recognition of unconstrained handwritten English words with character and ligature modeling. Viewing a handwritten word as an alternating sequence of characters and ligatures, a network of circularly interconnected hidden Markov models is constructed to model handwritten English words of indefinite length. Then the recognition problem is regarded as finding the maximal probability path in the network for given input sequence. From the path, optimal segmentation and associated character labels are obtained simultaneously without any explicit segmentation.
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Firdous, Arusa, Neha Pawar, Muheet Ahmed Butt, and Majid Zaman. "Review of Optical Character Recognition Techniques& Applications." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 30, 2017): 206. http://dx.doi.org/10.23956/ijarcsse/v7i7/0158.
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The Character Recognition of both keyboard typed and handwritten characters has still a long way to go in terms of research. Although significant success has been achieved in type written characters but in handwritten it is still to touch an appreciable level. Most of the methods that have been proposed in this regard have huge computational complexity. The proposed review provides an in depth review of the OCR methods which include segmentation, classification and recognition of characters independent in size and texture. The proposed review also provides the literature survey in a summarized manner providing a comparative analysis of various OCR techniques.
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Rehman, Muhammad Zubair, Nazri Mohd. Nawi, Mohammad Arshad, and Abdullah Khan. "Recognition of Cursive Pashto Optical Digits and Characters with Trio Deep Learning Neural Network Models." Electronics 10, no. 20 (October 15, 2021): 2508. http://dx.doi.org/10.3390/electronics10202508.
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Pashto is one of the most ancient and historical languages in the world and is spoken in Pakistan and Afghanistan. Various languages like Urdu, English, Chinese, and Japanese have OCR applications, but very little work has been conducted on the Pashto language in this perspective. It becomes more difficult for OCR applications to recognize handwritten characters and digits, because handwriting is influenced by the writer’s hand dynamics. Moreover, there was no publicly available dataset for handwritten Pashto digits before this study. Due to this, there was no work performed on the recognition of Pashto handwritten digits and characters combined. To achieve this objective, a dataset of Pashto handwritten digits consisting of 60,000 images was created. The trio deep learning Convolutional Neural Network, i.e., CNN, LeNet, and Deep CNN were trained and tested with both Pashto handwritten characters and digits datasets. From the simulations, the Deep CNN achieved 99.42 percent accuracy for Pashto handwritten digits, 99.17 percent accuracy for handwritten characters, and 70.65 percent accuracy for combined digits and characters. Similarly, LeNet and CNN models achieved slightly less accuracies (LeNet; 98.82, 99.15, and 69.82 percent and CNN; 98.30, 98.74, and 66.53 percent) for Pashto handwritten digits, Pashto characters, and the combined Pashto digits and characters recognition datasets, respectively. Based on these results, the Deep CNN model is the best model in terms of accuracy and loss as compared to the other two models.
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Kumar, J., and A. Roy. "DograNet – a comprehensive offline dogra handwriting character dataset." Journal of Physics: Conference Series 2251, no. 1 (April 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2251/1/012008.
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Abstract Handwritten Text Recognition is an important area of research because of growing demand to process and convert a huge data and information available in handwritten form to Digital form. The digital data instead of handwritten form can prove to be highly useful in different fields. Handwritten text recognition plays an important role in applications involved in, postal services, banks for cheque processing, searching of information and organization dealing with such applications. In text recognition application dataset of the specified script is required for training purpose. Datasets of the different languages could be found online but dataset of dogra script characters is still not available. This paper presents a Dogra handwriting character dataset which contains around 38690 character images etc grouped in 73 character classes extracted from 530 one-page handwritings of 265 individuals of having variable age, sex, qualification, location. The dogra character dataset would be freely accessible by scholars and researchers which could also be used for further recognition improvement and updating with more characters and word, Identification of writer, dogra word segmentation. Dogra dataset could also be used for extracting variation of handwriting according to age and gender.
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Das, Mamatarani, Mrutyunjaya Panda, and Shreela Dash. "Enhancing the Power of CNN Using Data Augmentation Techniques for Odia Handwritten Character Recognition." Advances in Multimedia 2022 (December 22, 2022): 1–13. http://dx.doi.org/10.1155/2022/6180701.
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The performance of any machine learning model largely depends on the type of input data provided. The higher the volume and variety of the data, the better the machine learning models get trained, thereby producing more accurate results. However, it is a challenging task to get high volume of data in some cases containing enough variety. Handwritten character recognition for Odia language is one of them. NITROHCS v1.0 for handwritten Odia characters and the ISI image database for handwritten Odia numerals are the standard Odia language datasets available for the research community. This paper shows the performance of five different machine learning models that uses a convolutional neural network to identify handwritten characters in response to handwritten datasets that are manipulated and expanded using several augmentation techniques to create variation and increase the volume of the data in the given dataset. These models, with the augmentation techniques discussed in the paper, even lead to a further increase in accuracy by approximately 1% across the models. The claims are supported by the results from the experiments done on the proposed convolutional neural network models on standard available Odia character and numeral data set.
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Uddin, Imran, Dzati A. Ramli, Abdullah Khan, Javed Iqbal Bangash, Nosheen Fayyaz, Asfandyar Khan, and Mahwish Kundi. "Benchmark Pashto Handwritten Character Dataset and Pashto Object Character Recognition (OCR) Using Deep Neural Network with Rule Activation Function." Complexity 2021 (March 4, 2021): 1–16. http://dx.doi.org/10.1155/2021/6669672.
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In the area of machine learning, different techniques are used to train machines and perform different tasks like computer vision, data analysis, natural language processing, and speech recognition. Computer vision is one of the main branches where machine learning and deep learning techniques are being applied. Optical character recognition (OCR) is the ability of a machine to recognize the character of a language. Pashto is one of the most ancient and historical languages of the world, spoken in Afghanistan and Pakistan. OCR application has been developed for various cursive languages like Urdu, Chinese, and Japanese, but very little work is done for the recognition of the Pashto language. When it comes to handwritten character recognition, it becomes more difficult for OCR to recognize the characters as every handwritten character’s shape is influenced by the writer’s hand motion dynamics. The reason for the lack of research in Pashto handwritten character data as compared to other languages is because there is no benchmark dataset available for experimental purposes. This study focuses on the creation of such a dataset, and then for the evaluation purpose, a machine is trained to correctly recognize unseen Pashto handwritten characters. To achieve this objective, a dataset of 43000 images was created. Three Feed Forward Neural Network models with backpropagation algorithm using different Rectified Linear Unit (ReLU) layer configurations (Model 1 with 1-ReLU Layer, Model 2 with 2-ReLU layers, and Model 3 with 3-ReLU Layers) were trained and tested with this dataset. The simulation shows that Model 1 achieved accuracy up to 87.6% on unseen data while Model 2 achieved an accuracy of 81.60% and 3% accuracy, respectively. Similarly, loss (cross-entropy) was the lowest for Model 1 with 0.15 and 3.17 for training and testing, followed by Model 2 with 0.7 and 4.2 for training and testing, while Model 3 was the last with loss values of 6.4 and 3.69. The precision, recall, and f-measure values of Model 1 were better than those of both Model 2 and Model 3. Based on results, Model 1 (with 1 ReLU activation layer) is found to be the most efficient as compared to the other two models in terms of accuracy to recognize Pashto handwritten characters.
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NISHIDA, HIROBUMI, and SHUNJI MORI. "A MODEL-BASED SPLIT-AND-MERGE METHOD FOR CHARACTER STRING RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 05 (October 1994): 1205–22. http://dx.doi.org/10.1142/s0218001494000607.
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Recognition of handwritten character strings is a challenging problem, because we need to cope with variations of shapes and touching/breaking of characters at the same time. A natural approach to recognizing such complex objects is as follows: The object is decomposed into segments, and meaningful partial shapes (shapes which are recognized as some characters) are constructed by merging segments locally. Then, a globally consistent interpretation of the object is determined from the combination of partial shapes. This approach can be referred to as a model-based split-and-merge method. Based on this idea, we present an algorithm for recognition and segmentation of character strings. We give systematic performance statistics by experiments using handwritten numerals. This algorithm can be applied to character strings composed of any number of characters and any type of touching or breaking, whether the number of constituent characters is known or unknown.