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Relevant bibliographies by topics / Handwritten Character Recognition (HCR)

Academic literature on the topic 'Handwritten Character Recognition (HCR)'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Handwritten Character Recognition (HCR)"

1

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 (November 30, 2022): 1156–62. http://dx.doi.org/10.22214/ijraset.2022.47540.

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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 seen. Many ways of recognizing handwritten data are available. The proposed research is based on 5*5 convolution neural network where the performance of the system has been enhanced in terms of accuracy, precision and recall the data set. The research utilized the real time photos. The processing approaches are followed by binarization, skeletonisation, dilution, resizing, segmentation and extraction. The character characteristics are sent to CNN to train the models after preprocessing. The research achieved 92% accuracy and time delay while detecting the real time Images.

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Hamdan, Yasir Babiker, and Sathish. "Construction of Statistical SVM based Recognition Model for Handwritten Character Recognition." June 2021 3, no. 2 (June 8, 2021): 92–107. http://dx.doi.org/10.36548/jitdw.2021.2.003.

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There are many applications of the handwritten character recognition (HCR) approach still exist. Reading postal addresses in various states contains different languages in any union government like India. Bank check amounts and signature verification is one of the important application of HCR in the automatic banking system in all developed countries. The optical character recognition of the documents is comparing with handwriting documents by a human. This OCR is used for translation purposes of characters from various types of files such as image, word document files. The main aim of this research article is to provide the solution for various handwriting recognition approaches such as touch input from the mobile screen and picture file. The recognition approaches performing with various methods that we have chosen in artificial neural networks and statistical methods so on and to address nonlinearly divisible issues. This research article consisting of various approaches to compare and recognize the handwriting characters from the image documents. Besides, the research paper is comparing statistical approach support vector machine (SVM) classifiers network method with statistical, template matching, structural pattern recognition, and graphical methods. It has proved Statistical SVM for OCR system performance that is providing a good result that is configured with machine learning approach. The recognition rate is higher than other methods mentioned in this research article. The proposed model has tested on a training section that contained various stylish letters and digits to learn with a higher accuracy level. We obtained test results of 91% of accuracy to recognize the characters from documents. Finally, we have discussed several future tasks of this research further.

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Anang Aris Widodo, Muchammad Yuska Izza Mahendra, and Mohammad Zoqi Sarwani. "Recognition of Korean Alphabet (Hangul) Handwriting into Latin Characters Using Backpropagation Method." International Journal of Artificial Intelligence & Robotics (IJAIR) 3, no. 2 (November 30, 2021): 50–57. http://dx.doi.org/10.25139/ijair.v3i2.4210.

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The popularity of Korean culture today attracts many people to learn everything about Korea, especially in learning the Korean language. To learn Korean, you must first know Korean letters (Hangul), which are non-Latin characters. Therefore, a digital approach is needed to recognize handwritten Korean (Hangul) words easily. Handwritten character recognition has a vital role in pattern recognition and image processing for handwritten Character Recognition (HCR). The backpropagation method trains the network to balance the network's ability to recognize the patterns used during training and the network's ability to respond correctly to input patterns that are similar but not the same as the patterns used during training. This principle is used for character recognition of Korean characters (Hangul), a sub-topic in fairly complex pattern recognition. The results of the calculation of the backpropagation artificial neural network with MATLAB in this study have succeeded in identifying 576 image training data and 384 Korean letter testing data (Hangul) quite well and obtaining a percentage result of 80.83% with an accuracy rate of all data testing carried out on letters. Korean (Hangul).

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Li, Lei, Xue Gao, and Lianwen Jin. "HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm." Scientific Programming 2018 (September 5, 2018): 1–16. http://dx.doi.org/10.1155/2018/6509275.

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Handwritten character recognition (HCR) is a mainstream mobile device input method that has attracted significant research interest. Although previous studies have delivered reasonable recognition accuracy, it remains difficult to directly embed the advanced HCR service into mobile device software and obtain excellent but fast results. Cloud computing is a relatively new online computational resource provider which can satisfy the elastic resource requirements of the advanced HCR service with high-recognition accuracy. However, owing to the delay sensitivity of the character recognition service, the performance loss in the traditional cloud virtualization technology (e.g., kernel-based virtual machine (KVM)) may impair the performance. In addition, the improper computational resource scheduling in cloud computing impairs not only the performance but also the resource utilization. Thus, the HCR online service is required to guarantee the performance and improve the resource utilization of the HCR service in cloud computing. To address these problems, in this paper, we propose an HCR container as a service (HCRCaaS) in cloud computing. We address several key contributions: (1) designing an HCR engine on the basis of deep convolution neutral networks as a demo for an advanced HCR engine with better recognition accuracy, (2) providing an isolated lightweight runtime environment for high performance and easy expansion, and (3) designing a greedy resource scheduling algorithm based on the performance evaluation to optimize the resource utilization under a quality of service (QoS) guaranteeing. Experimental results show that our system not only reduces the performance loss compared with traditional cloud computing under the advanced HCR algorithm but also improves the resource utilization appropriately under the QoS guaranteeing. This study also provides a valuable reference for other related studies.

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Sonkusare, Manoj, and Narendra Sahu. "A Survey on Handwritten Character Recognition (HCR) Techniques for English Alphabets." Advances in Vision Computing: An International Journal 3, no. 1 (March 30, 2016): 1–12. http://dx.doi.org/10.5121/avc.2016.3101.

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Mukti, Mousumi Hasan, Quazi Saad-Ul-Mosaher, and Khalil Ahammad. "Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric." European Journal of Engineering Research and Science 3, no. 7 (July 31, 2018): 67. http://dx.doi.org/10.24018/ejers.2018.3.7.831.

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Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.

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Mukti, Mousumi Hasan, Quazi Saad-Ul-Mosaher, and Khalil Ahammad. "Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric." European Journal of Engineering and Technology Research 3, no. 7 (July 31, 2018): 67–73. http://dx.doi.org/10.24018/ejeng.2018.3.7.831.

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Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.

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Guha, Riya, Nibaran Das, Mahantapas Kundu, Mita Nasipuri, and K. C. Santosh. "DevNet: An Efficient CNN Architecture for Handwritten Devanagari Character Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (April 30, 2020): 2052009. http://dx.doi.org/10.1142/s0218001420520096.

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The writing style is a unique characteristic of a human being as it varies from one person to another. Due to such diversity in writing style, handwritten character recognition (HCR) under the purview of pattern recognition is not trivial. Conventional methods used handcrafted features that required a-priori domain knowledge, which is always not feasible. In such a case, extracting features automatically could potentially attract more interests. For this, in the literature, convolutional neural network (CNN) has been a popular approach to extract features from the image data. However, state-of-the-art works do not provide a generic CNN model for character recognition, Devanagari script, for instance. Therefore, in this work, we first study several different CNN models on publicly available handwritten Devanagari characters and numerals datasets. This means that our study is primarily focusing on comparative study by taking trainable parameters, training time and memory consumption into account. Later, we propose and design DevNet, a modified CNN architecture that produced promising results, since computational complexity and memory space are our primary concerns in design.

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Chacko, Binu P. "Comparison of Feature Extraction Techniques for Pattern Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 5511–17. http://dx.doi.org/10.22214/ijraset.2021.36214.

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Pattern recognition is a challenging task in research field for the last few decades. Many researchers have worked on areas such as computer vision, speech recognition, document classification, and computational biology to tackle complex research problems. In this article, a pattern recognition problem for handwritten Malayalam character is presented. This system goes through two different stages of HCR namely, feature extraction and classification. Three feature extraction techniques – wavelet transform, zoning, division point – are used in this study. Among these, division is point is able to show best discriminative power using SVM classifier. All the experiments are conducted on size normalized and binarized images of isolated Malayalam characters.

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Ampelakiotis, Vaios, Isidoros Perikos, Ioannis Hatzilygeroudis, and George Tsihrintzis. "Optical Recognition of Handwritten Logic Formulas Using Neural Networks." Electronics 10, no. 22 (November 12, 2021): 2761. http://dx.doi.org/10.3390/electronics10222761.

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In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.

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Dissertations / Theses on the topic "Handwritten Character Recognition (HCR)"

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Chai, Sin-Kuo. "Multiclassifier neural networks for handwritten character recognition." Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1174331633.

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Clarke, Eddie. "A novel approach to handwritten character recognition." Thesis, University of Nottingham, 1995. http://eprints.nottingham.ac.uk/14035/.

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A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules.

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Xu, Zhengyan, and Yibing Zhou. "Specific Handwritten Chinese Character Recognition Based on Artificial Intelligence." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-14599.

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As internet techniques are developing more and more quickly, internet becomes the main way to communicate with the outside world. In this case, written information on paper needs to be converted to digital information urgently, increasing the need for handwritten character recognition. The aim of this work is to discuss methods that can be used to recognize handwritten Chinese characters. We study geometric features and clustering of handwritten Chinese characters from three aspects, which are handwritten character preprocessing, feature extraction and clustering. To test the correctness of our method, an application was built that could learn to recognize five medium-hard handwritten Chinese characters by using a neural network.

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Sawhney, Sumeet S. "Distance measurements and their combination in handwritten character recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ59339.pdf.

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Ansari, Nasser. "Handwritten character recognition by using neural network based methods." Ohio : Ohio University, 1992. http://www.ohiolink.edu/etd/view.cgi?ohiou1172080742.

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陳國評 and Kwok-ping Chan. "Fuzzy set theoretic approach to handwritten Chinese character recognition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B30425876.

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Sahai, Anant. "Handwritten character recognition using the minimum description length principle." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11015.

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Shi, Daming. "An active radical approach to handwritten Chinese character recognition." Thesis, University of Southampton, 2002. https://eprints.soton.ac.uk/257379/.

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Manley-Cooke, Peter. "Handwritten character recognition using a multi-classifier neuro-fuzzy framework." Thesis, University of East Anglia, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433914.

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Kassel, Robert H. "A comparison of approaches to on-line handwritten character recognition." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11407.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Includes bibliographical references (p. 155-163).
by Robert Howard Kassel.
Ph.D.

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Books on the topic "Handwritten Character Recognition (HCR)"

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Li, Xiaolin. On-line handwritten Kanji character recognition. Birmingham: University of Birmingham, 1994.

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Hastie, Trevor. Handwritten digit recognition via deformable prototypes. Toronto: University of Toronto, Dept. of Statistics, 1992.

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Pirlo, Giuseppe, Donato Impedovo, and Michael C. Fairhurst. Advances in Digital Handwritten Signature Processing: A Human Artefact for E-Society. World Scientific Publishing Co Pte Ltd, 2014.

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4

Chang, Iris J. A handwritten numeral recognition system with multi-level decision scheme (MDS). 1986.

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Book chapters on the topic "Handwritten Character Recognition (HCR)"

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Álvarez, D., R. Fernández, and L. Sánchez. "Stroke Based Handwritten Character Recognition." In Lecture Notes in Computer Science, 343–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28942-2_31.

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Tiwari, Usha, Monika Jain, and Shabana Mehfuz. "Handwritten Character Recognition—An Analysis." In Lecture Notes in Electrical Engineering, 207–12. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0665-5_18.

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Yashoda, S. K. Niranjan, and V. N. Manjunath Aradhya. "Transform-Based Trilingual Handwritten Character Recognition." In Frontiers in Intelligent Computing: Theory and Applications, 293–96. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9920-6_30.

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Fox, Richard, and Steven Brownfield. "Applying Context to Handwritten Character Recognition." In Advances in Intelligent Systems and Computing, 40–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19810-7_5.

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Hogervorst, A. C. R., M. K. van Dijk, P. C. M. Verbakel, and C. Krijgsman. "Handwritten character recognition using neural networks." In Neural Networks: Artificial Intelligence and Industrial Applications, 337–43. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3087-1_62.

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Inunganbi, Sanasam, and Robin Singh Katariya. "Transfer Learning for Handwritten Character Recognition." In Intelligent Sustainable Systems, 691–99. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6369-7_63.

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Singh, Jaisal, Srinivasan Natesan, Marcin Paprzycki, and Maria Ganzha. "Experimenting with Assamese Handwritten Character Recognition." In Big-Data-Analytics in Astronomy, Science, and Engineering, 219–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96600-3_16.

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Brodowska, Magdalena. "An Oversegmentation Method for Handwritten Character Segmentation." In Computer Recognition Systems 4, 517–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_54.

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Zhang, Xiaoyi, Tianwei Wang, Jiapeng Wang, Lianwen Jin, Canjie Luo, and Yang Xue. "ChaCo: Character Contrastive Learning for Handwritten Text Recognition." In Frontiers in Handwriting Recognition, 345–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_24.

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Cano, Javier, Juan-Carlos Perez-Cortes, Joaquim Arlandis, and Rafael Llobet. "Training Set Expansion in Handwritten Character Recognition." In Lecture Notes in Computer Science, 548–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-70659-3_57.

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Conference papers on the topic "Handwritten Character Recognition (HCR)"

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Rajyagor, Bhargav, and Rajnish Rakholia. "Isolated Gujarati Handwritten Character Recognition (HCR) using Deep Learning (LSTM)." In 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2021. http://dx.doi.org/10.1109/icecct52121.2021.9616652.

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Wahi, Amitabh, S. Sundaramurthy, and Poovizhi P. "Handwritten Tamil character recognition." In 2013 Fifth International Conference on Advanced Computing (ICoAC). IEEE, 2013. http://dx.doi.org/10.1109/icoac.2013.6921982.

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Aggarwal, Ashutosh, and Karamjeet Singh. "Handwritten Gurmukhi character recognition." In 2015 International Conference on Computer, Communication and Control (IC4). IEEE, 2015. http://dx.doi.org/10.1109/ic4.2015.7375678.

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sarma, Parismita, Chandan Kumar Chourasia, and Manashjyoti Barman. "Handwritten Assamese Character Recognition." In 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). IEEE, 2019. http://dx.doi.org/10.1109/i2ct45611.2019.9033603.

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Mishra, Mayank, Tanupriya Choudhury, and Tanmay Sarkar. "Devanagari Handwritten Character Recognition." In 2021 IEEE India Council International Subsections Conference (INDISCON). IEEE, 2021. http://dx.doi.org/10.1109/indiscon53343.2021.9582192.

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Tao, Ngo Quoc, and Pham Van Hung. "Online Continues Vietnamese Handwritten Character Recognition Based on Microsoft Handwritten Character Recognition Library." In APCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems. IEEE, 2006. http://dx.doi.org/10.1109/apccas.2006.342286.

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Hanmandlu, M., K. R. Murali Mohan, and H. Kumar. "Neural based handwritten character recognition." In Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318). IEEE, 1999. http://dx.doi.org/10.1109/icdar.1999.791769.

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Bhandare, Minakshi Sanjay, and Anuradha Sopan Kakade. "Handwritten (Marathi) compound character recognition." In 2015 International Conference on Innovations in Information,Embedded and Communication Systems (ICIIECS). IEEE, 2015. http://dx.doi.org/10.1109/iciiecs.2015.7193125.

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Xiaoou Tang and Feng Lin. "Video-based handwritten character recognition." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1004732.

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Tang, Xiaoou, and Feng Lin. "Video-based handwritten character recognition." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745471.

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Reports on the topic "Handwritten Character Recognition (HCR)"

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Grother, Patrick J. Karhunen Loeve feature extraction for neural handwritten character recognition. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4824.

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Fuller, J. J., A. Farsaie, and T. Dumoulin. Handwritten Character Recognition Using Feature Extraction and Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada238294.

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