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Relevant bibliographies by topics / Handwritten character recognition

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Academic literature on the topic 'Handwritten character recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 June 2025

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Journal articles on the topic "Handwritten character recognition"

1

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

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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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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 1000 years, where the consonants and vowels are symmetric in nature and also curvy, therefore, the recognition of Kannada characters online is very difficult. Thus, it is essential to overcome the above-mentioned complications presented in the classical Kannada handwritten character recognition model. The recognition of characters from Kannada Scripts is also difficult. Hence, this work aims to design a new Kannada handwritten character recognition framework using deep learning techniques from Kannada scripts. There are two steps to be followed in the proposed model that is collection of images and classification of handwritten characters. At first, essential handwritten Kannada characters are collected from the benchmark resources. Next, the acquired handwritten Kannada images are offered to the handwritten Kannada character recognition phase. Here, Kannada character recognition is performed using Serial Dilated Cascade Network (SDCN), which utilized the Visual Geometry Group 16 (VGG16) and Deep Temporal Convolution Network (DTCN) technique for the observation. When compared to the baseline recognition works, the proposed handwritten Kannada character recognition model achieves a significantly higher performance rate.

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

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<strong>Character recognitions play a wide role in the fast moving world with the growing technology,by providing more scope to perform research in OCR techniques. In the field of pattern recognition Devnagari handwritten character recognition is one of the challenging research area. Character recognition is defined as electronic translation of scanned images of handwritten or printed text into a machine encoded text. In this paper proposed an off line handwritten Devnagari character recognition technique with the use of feed forward neural network. For training the neural network a handwritten Devnagari character which is resized into 20x30 pixels is used. The same character is then given to the neural network as input with different set of neurons in hidden layer after the training process,and their recognition accuracy rate is calculated and compared for different Devnagari characters. Good recognition accuracy rates has been given by the proposed system comparable to that of other hand written character recognition systems.</strong> <strong>https://www.ijiert.org/paper-details?paper_id=141157</strong>

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

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

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Digitizing handwritten documents and enabling efficient information processing and retrieval require systems that can recognize handwritten characters. This research offers a unique approach for handwritten character detection using state-of-the-art machine learning algorithms. The proposed technique automatically extracts discriminative features from photos of handwritten characters using convolutional neural networks (CNNs). These attributes are then used by a classifier to determine which characters are related. The dataset used for training and assessment is made up of a large collection of handwritten characters gathered under various writing styles, sizes, and orientations in order to guarantee the durability and generalization power of the model. To enhance its quality and diversity, the training data is put through a rigorous preparation procedure that includes picture augmentation, noise removal, and normalization. The studies' results demonstrate how well and precisely the proposed system can recognize handwritten characters in a range of languages and writing styles. The system performs competitively compared to state-of-the-art methods and demonstrates robustness against variations in handwriting style and quality. Furthermore, the system has potential in terms of efficiency and scalability, making it suitable for real-time applications such as document digitalization, handwritten word recognition in electronic devices, and automatic form processing.

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

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

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

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The application of pattern recognition technology enables us to solve various human-computer interaction problems that were difficult to solve before. Handwritten Chinese character recognition, as a hot research object in image pattern recognition, has many applications in people’s daily life, and more and more scholars are beginning to study off-line handwritten Chinese character recognition. This paper mainly studies the recognition of handwritten Chinese characters by BP (Back Propagation) neural network. Establish a handwritten Chinese character recognition model based on BP neural network, 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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Santosh, Acharya, Dhungel Shashank, and Kr. Jha Ashish. "Nepali Handwritten Character Recognition System." Advancement in Image Processing and Pattern Recognition 5, no. 3 (2022): 1–6. https://doi.org/10.5281/zenodo.7472398.

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Even if the technological and digital world is expanding more quickly, there are still many things that are lacking. What a wonderful thing it would be to be able to trust machines to scan any handwritten characters into digital representation. The method for doing this is called optical character recognition (OCR), but there is still much room for improvement. Although there has been work done on it, the technique developed for one language cannot be applied to another due to language variations. Nepali is not a language that is frequently used online. Perhaps this is why there are fewer OCR systems developed using this language. We have made an effort to improve on it so that Nepali characters can be recognized. Basically, the idea is to use a camera to scan Nepali handwriting from hard copy paper, locate the regions in the image where the characters are present, segment those localized parts into characters, and then digitally display each predicted segmented character.

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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 further differ based on factors such as the writer's age and birthplace. In this paper, we propose a modified ResNet-34 architecture, a convolutional neural network (CNN) model, to identify Bangla handwritten characters accurately. The proposed approach is validated using a merged subset of two popular Bangla handwritten  datasets. Through our technique, we achieve state-of-the- art recognition performance. Experimental results  demonstrate that the suggested model attains an average accuracy of 98.70% for Bangla handwritten vowels, 97.34% for consonants, and 99.02% for numeric characters. Additionally, when applied to a mixed dataset comprising vowels, consonants, and numeric characters, the proposed model achieves an overall accuracy of 97%. This research contributes to advancing digital manufacturing systems by addressing the challenge of Bangla Handwritten Character Recognition, offering a high-performing solution based on a modified ResNet-34 architecture. The achieved recognition accuracy signifies significant progress in this field, potentially paving the way for enhanced automation and efficiency in various applications that involve processing Bangla handwritten text. Keywords:- Handwritten Character Recognition; ResNet; Optical Character Recognition; Computer Vision; Convolutional Neural Networks.

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Dissertations / Theses on the topic "Handwritten character recognition"

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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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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 properties of handwritten characters. A new skeleton end extension algorithm is also introduced. The MSA mending method is combined with a skeleton-based recognition algorithm to verify its efficiency. Experiment results indicate that significant improvement has been achieved. The feature extraction methods in OCR are analyzed by comparing their effectiveness in different situations. Several factors and their relation with the effectiveness of each feather extraction method are investigated. A dynamic feature extraction method is developed to improve the performance of hybrid OCR systems. Hybrid methods for handwritten numeral recognition are then described, which combine two compensatory recognisers by analyzing their performance for several aspects. The different performances of the two algorithms for broken, connected or slanted numerals. and the rneasurement—level decision provided by the neural network algorithm are detected and combined to develop matching rules for each recognition method. Five combination methods are developed to meet different requirements. Experiments with a large number of testing data show satisfactory results for the approach. Finally, a generic method for compressing and indexing multi—copy form documents is developed using template extraction and matching (TEM) strategies and OCR. De—skewing, location and distortion adjusting of form images are employed to realise the TEM method for practical applications. A statistical template extraction algorithm is developed using greyscale images created by overlapping a number of binary form images. The TEM method exploits the cmnponent—Ievel redundancy found in multi—copy form documents and reaches a high compression rate while keeping the original resolution and readability.

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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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Books on the topic "Handwritten character recognition"

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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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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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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"

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Álvarez, D., R. Fernández, and L. Sánchez. "Stroke Based Handwritten Character Recognition." In Lecture Notes in Computer Science. 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. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0665-5_18.

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Singh, Harshita, Sudhir Singh, and A. K. Mohapatra. "Handwritten character recognition using CNN." In Data Science & Exploration in Artificial Intelligence. CRC Press, 2025. https://doi.org/10.1201/9781003589273-33.

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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. 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. 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. 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. 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. 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. 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. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_24.

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Conference papers on the topic "Handwritten character recognition"

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Rodrigues, Anisha P., Akshay Prabhu K, Shailesh U. Acharya, et al. "Local Language Handwritten Character Recognition." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10987295.

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Yimer, Hailemicael Lulseged, Hailegabriel Dereje Degefa, Marco Cristani, and Federico Cunico. "Learning Based Ge'ez Character Handwritten Recognition." In 2024 IEEE International Multi-Conference on Smart Systems & Green Process (IMC-SSGP). IEEE, 2024. https://doi.org/10.1109/imc-ssgp63352.2024.10919767.

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Devi Sri, Gorla Naga, Shaik Khaleel Ahmed, Mandem Srujanasree, and Shaik Shafiya. "A CNN-Based Handwritten English Character Recognition." In 2024 International Conference on Cybernation and Computation (CYBERCOM). IEEE, 2024. https://doi.org/10.1109/cybercom63683.2024.10803137.

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Tapu, Tasmi Khair, Farhan Faiaz, Anika Nawer, and Sadia Rahman Payel. "Bangla Handwritten Character Recognition using Vision Transformer." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11014037.

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Pallempati, Ishita, D. Vijaya Lakshmi, and M. Swami Das. "Handwritten Character Recognition and Vehicle Number Recognition using OCR Method." In 2024 Third International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON). IEEE, 2024. https://doi.org/10.1109/teeccon64024.2024.10939189.

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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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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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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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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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Reports on the topic "Handwritten character recognition"

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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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