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Relevant bibliographies by topics / Handwritten characters

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Academic literature on the topic 'Handwritten characters'

Author: Grafiati

Published: 18 May 2024

Last updated: 31 July 2025

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Journal articles on the topic "Handwritten characters"

1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Dissertations / Theses on the topic "Handwritten characters"

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Al-Emami, Samir Yaseen Safa. "Machine recognition of handwritten and typewritten Arabic characters." Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359173.

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Wang, Jianguo. "Off-line computer recognition of unconstrained handwritten characters." Thesis, The University of Sydney, 2001. https://hdl.handle.net/2123/27805.

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This thesis presents several techniques for improving the performance of off—line Optical Character Recognition (OCR) systems: broken character mending and recognition, feature extraction methods in OCR and hybrid methods for handwritten numeral recognition. As an application, form document image compression and indexing is also introduced. Broken characters mending techniques are investigated first. A macrostrtrcture analysis (MSA) mending method is proposed based on skeleton and boundary information and macrostructure analysis that investigates the stroke tendency and other propert

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Ziogas, Georgios. "Classifying Handwritten Chinese Characters using Convolutional Neural Networks." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-371526.

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Image recognition applications have been increasingly gaining popularity, as computer hardware was getting more powerful and cheaper. This increase in computational resources, led researchers even closer to their target on creating algorithms that could achieve high accuracy in image recognition tasks. These algorithms are applied in many different fields, such as in medical images analysis and object recognition in real-time applications.Previously studies have shown that among many image recognition algorithms, artificial neural networks and specifically deep neural networks, perform outstan

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Mandal, Rakesh Kumar. "Development of Neural Network techniques to recognize handwritten characters." Thesis, University of North Bengal, 2015. http://ir.nbu.ac.in/handle/123456789/1790.

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Hosseini, Habib Mir Mohamad. "Analysis and recognition of Persian and Arabic handwritten characters /." Title page, contents and abstract only, 1997. http://web4.library.adelaide.edu.au/theses/09PH/09phh8288.pdf.

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Mitchell, John. "Computer based analysis of handwritten characters for hand-eye coordination therapy." Thesis, University of Kent, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358603.

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Sae-Tang, Sutat. "A systematic study of offline recognition of Thai printed and handwritten characters." Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/206079/.

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Thai characters pose some unique problems, which differ from English and other oriental scripts. The structure of Thai characters consists of small loops combined with curves and there is an absence of spaces between each word and sentence. In each line, moreover, Thai characters can be composed on four levels, depending on the type of character being written. This research focuses on OCR for the Thai language: printed and offline handwritten character recognition. An attempt to overcome the problems by simple but effective methods is the main consideration. A printed OCR developed by the Nati

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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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Wang, Yongqiang. "A study on structured covariance modeling approaches to designing compact recognizers of online handwritten Chinese characters." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42664305.

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Wang, Yongqiang, and 王永強. "A study on structured covariance modeling approaches to designing compact recognizers of online handwritten Chinese characters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42664305.

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Books on the topic "Handwritten characters"

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Yang, Xiaoping. Qing dai shou xie wen xian zhi su zi yan jiu: A study on popular form of characters in the handwritten documents of the Qing dynasty. Beijing shi fan da xue chu ban she, 2019.

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

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

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Alcott, Louisa May. Jo's Boys : And How They Turned Out: A Sequel to Little Men - Handwritten Style. Independently Published, 2021.

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Burroughs, Edgar Rice. Tarzan the Untamed: Handwritten Style. Independently Published, 2021.

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Burroughs, Edgar Rice. Tarzan the Untamed: Handwritten Style. Independently Published, 2021.

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Burroughs, Edgar Rice. Tarzan the Terrible: Handwritten Style. Independently Published, 2021.

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Burroughs, Edgar Rice. Beasts of Tarzan: Handwritten Style. Independently Published, 2021.

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Conan, Doyle A. Poison Belt: Handwritten Style. Independently Published, 2021.

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Conan, Doyle Arthur. Poison Belt: Handwritten Style. Independently Published, 2021.

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Book chapters on the topic "Handwritten characters"

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Watson, Mark. "Recognition of Handwritten Characters." In Common LISP Modules. Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3186-8_6.

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Suen, Ching Y. "Automatic Recognition of Handwritten Characters." In Fundamentals in Handwriting Recognition. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78646-4_4.

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Borgohain, Olimpia, Pramod Kumar, and Saurabh Sutradhar. "Recognition of Handwritten Assamese Characters." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7041-2_17.

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Shimomura, Haruna, and Hiroyoshi Miwa. "Automatic Generation of Handwritten Style Characters Including Untrained Characters." In Advances in Intelligent Networking and Collaborative Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40971-4_2.

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Bhattacharya, U., M. Shridhar, and S. K. Parui. "On Recognition of Handwritten Bangla Characters." In Computer Vision, Graphics and Image Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_73.

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Halder, Chayan, Sk Md Obaidullah, Jaya Paul, and Kaushik Roy. "Writer Verification on Bangla Handwritten Characters." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2653-6_4.

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Ojumah, Samuel, Sanjay Misra, and Adewole Adewumi. "A Database for Handwritten Yoruba Characters." In Data Science and Analytics. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8527-7_10.

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Zargar, Hisham, Ruba Almahasneh, and László T. Kóczy. "Automatic Recognition of Handwritten Urdu Characters." In Studies in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74970-5_19.

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Naga Manisha, C., Y. K. Sundara Krishna, and E. Sreenivasa Reddy. "Glyph Segmentation for Offline Handwritten Telugu Characters." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3223-3_21.

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Li, Wei, Xiaoxuan He, Chao Tang, et al. "Handwritten Numbers and English Characters Recognition System." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48499-0_18.

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Conference papers on the topic "Handwritten characters"

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Wasalwar, Yash Prashant, Kishan Singh Bagga, PVRR Bhogendra Rao, and Snehlata Dongre. "Handwritten Character Recognition of Telugu Characters." In 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). IEEE, 2023. http://dx.doi.org/10.1109/i2ct57861.2023.10126377.

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Koerich, A. L., and P. R. Kalva. "Unconstrained handwritten character recognition using metaclasses of characters." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530112.

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Joe, Kevin George, Meghna Savit, and K. Chandrasekaran. "Offline Character recognition on Segmented Handwritten Kannada Characters." In 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019. http://dx.doi.org/10.1109/gcat47503.2019.8978320.

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Bratić, Diana, and Nikolina Stanić Loknar. "AI driven OCR: Resolving handwritten fonts recognizability problems." In 10th International Symposium on Graphic Engineering and Design. University of Novi Sad, Faculty of technical sciences, Department of graphic engineering and design,, 2020. http://dx.doi.org/10.24867/grid-2020-p82.

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Optical Character Recognition (OCR) is the electronic or mechanical conversion of images of typed, handwritten, or printed text into machine-encoded text. Advanced systems are capable to produce a high degree of recognition accuracy for most technic fonts, but when it comes to handwritten forms there is a problem occur in recognizing certain characters and limitations with conventional OCR processes persist. It is most pronounced in ascenders (k, b, l, d, h, t) and descenders (g, j, p, q, y). If the characters are linked by ligatures, the ascending and descending strokes are even less recogniz

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Chaithra, D., and K. Indira. "Handwritten online character recognition for single stroke Kannada characters." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2017. http://dx.doi.org/10.1109/rteict.2017.8256657.

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Kato, Takahito. "Evaluation system for handwritten characters." In SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, edited by Donald P. D'Amato, Wolf-Ekkehard Blanz, Byron E. Dom, and Sargur N. Srihari. SPIE, 1992. http://dx.doi.org/10.1117/12.130275.

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Hussien, Rana S., Azza A. Elkhidir, and Mohamed G. Elnourani. "Optical Character Recognition of Arabic handwritten characters using Neural Network." In 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE). IEEE, 2015. http://dx.doi.org/10.1109/iccneee.2015.7381412.

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Prameela, N., P. Anjusha, and R. Karthik. "Off-line Telugu handwritten characters recognition using optical character recognition." In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2017. http://dx.doi.org/10.1109/iceca.2017.8212801.

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Islam Bhuiyan, Md Raihanul, Mahin Shahriar Efaz, Tanjim Reza, Aditi Saha Ria, Md Tanzim Reza, and Muhammad Iqbal Hossain. "Segmentation of Bangla Compound Characters: Underlying Simple Character Detection from Handwritten Compound Characters Using YOLOv8." In 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceeict62016.2024.10534334.

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Macwan, Swital J., and Archana N. Vyas. "Classification of offline gujarati handwritten characters." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275831.

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Reports on the topic "Handwritten characters"

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Grother, Patrick J. Karhunen Loeve feature extraction for neural handwritten character recognition. 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. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada238294.

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Griffiths, Rachael. Transkribus in Practice: Improving CER. Verlag der Österreichischen Akademie der Wissenschaften, 2022. http://dx.doi.org/10.1553/tibschol_erc_cog_101001002_griffiths_cer.

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This paper documents ongoing efforts to enhance the accuracy of Handwritten Text Recognition (HTR) models using Transkribus, focusing on the transcription of Tibetan cursive (dbu med) manuscripts from the 11th to 13th centuries within the framework of the ERC-funded project, The Dawn of Tibetan Buddhist Scholasticism (11th-13th C.) (TibSchol). It presents the steps taken to improve the Character Error Rate (CER) of the HTR models, the results achieved so far, and considerations for those working on similar projects.

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