Relevant bibliographies by topics / Handwritten Bangla digit recognition

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  2. Dissertations / Theses
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Academic literature on the topic 'Handwritten Bangla digit recognition'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Handwritten Bangla digit recognition"

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Bhattacharjee, Indronil. "An Efficient Method for Bangla Handwritten Digit Recognition Using Convolutional Neural Network." Technium: Romanian Journal of Applied Sciences and Technology 18 (December 1, 2023): 65–74. http://dx.doi.org/10.47577/technium.v18i.10243.

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Handwritten digit recognition is a fundamental problem in the field of computer vision and pattern recognition. This paper presents a Convolutional Neural Network (CNN) approach for recognizing handwritten Bangla digits. The proposed method utilizes a dataset of handwritten Bangla digit images and trains a CNN model to classify these digits accurately. The dataset is preprocessed to enhance the quality of the images and make them suitable for training the CNN model. The trained model is then tested on a separate test dataset to evaluate its performance in terms of accuracy. With the Ekush: Ban
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Chayti, Saha, Masuma Fozilatunnesa, Ahammad Khalil, Shahriar Muzammel Chowdhury, and Mohibullah Md. "Real time Bangla Digit Recognition through Hand Gestures on Air Using Deep Learning and OpenCV." International Journal of Current Science Research and Review 05, no. 02 (2022): 435–45. https://doi.org/10.5281/zenodo.6092684.

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Abstract : Digit Recognition in real time through hand gestures has achieved great attention in machine learning and computer vision applications. This article focuses on identifying Bangla numerals in the air using hand motions. This research leads to the stairwell, allowing for more investigation in the same subject for various Bangla characters and even phrases. The major issue, however, is coping with the wide range of handwriting styles employed by various users. Many studies have been done on the identification of Bangla handwritten digits, but none has proven successful at recognizing B
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Samanta, Roopkatha, Soulib Ghosh, Agneet Chatterjee, and Ram Sarkar. "A Novel Approach Towards Handwritten Digit Recognition Using Refraction Property of Light Rays." International Journal of Computer Vision and Image Processing 10, no. 3 (2020): 1–17. http://dx.doi.org/10.4018/ijcvip.2020070101.

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Due to the enormous application, handwritten digit recognition (HDR) has become an extremely important domain in optical character recognition (OCR)-related research. The predominant challenges faced in this domain include different photometric inconsistencies together with computational complexity. In this paper, the authors proposed a language invariant shape-based feature descriptor using the refraction property of light rays. It is to be noted that the proposed approach is novel as an adaptation of refraction property is completely new in this domain. The proposed method is assessed using
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Hossain, Md Shahadat, Md Anwar Hossain, AFM Zainul Abadin, and Md Manik Ahmed. "Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network." Engineering International 9, no. 2 (2021): 73–84. http://dx.doi.org/10.18034/ei.v9i2.551.

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The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition probl
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Shishir, Sarker, Sarker Songita, Rahman Sohanur, and Md. Ismail Jabiullah Dr. "A Lenet-5 Based Bangla Handwritten Digit Recognition Framework." Advancement in Image Processing and Pattern Recognition 2, no. 3 (2019): 1–7. https://doi.org/10.5281/zenodo.3564243.

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<em>Hand composed Digit recognition in Bangla language is a valuable beginning stage for creating an Optical Character Recognition in the Bengali language. Be that as it may, Absence of huge and honest data collection, recognition of Bangla digit was not build already. In any case, in this outline, a colossal &amp; honest data source known as NumtaDB is utilized for recognition of Bengali digits. The troublesome endeavour is connected to getting the solid presentation and high precision for gigantic, fair, common, natural and particularly extended NumtaDB dataset. So various sorts of pre-proce
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Singh, Pawan Kumar, Ram Sarkar, and Mita Nasipuri. "A Study of Moment Based Features on Handwritten Digit Recognition." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/2796863.

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Handwritten digit recognition plays a significant role in many user authentication applications in the modern world. As the handwritten digits are not of the same size, thickness, style, and orientation, therefore, these challenges are to be faced to resolve this problem. A lot of work has been done for various non-Indicscripts particularly, in case ofRoman, but, in case ofIndicscripts, the research is limited. This paper presents a script invariant handwritten digit recognition system for identifying digits written in five popular scripts of Indian subcontinent, namely,Indo-Arabic,Bangla,Deva
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Hossain, Afsana, Md Sabbir Hasan, Md Mujtaba Asif, and Amit Kumar Das. "Performance Analysis On Bangla Handwritten Digit Recognition Using CNN And Transfer Learning." International Journal of Advanced Networking and Applications 13, no. 01 (2021): 4809–15. http://dx.doi.org/10.35444/ijana.2021.13101.

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De, Shankha, and Arpana Rawal. "BANGLA HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTION NEURAL NETWORK." ICTACT Journal on Soft Computing 12, no. 2 (2022): 2545–50. http://dx.doi.org/10.21917/ijsc.2022.0364.

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Since, last one-decade, numerous deep learning models have been designed to resolve handwritten character recognition task in languages, namely, English, Chinese, Arabic, Japanese and Russian. Recognition of Bengali handwritten character from document image datasets is undoubtedly an open challenging task. Due to the advancement of neural network, many models have been developed and it is improving performance. The LeNet is a pioneering work in the field handwritten document image recognition specially hand written digits from the images by using CNN. This paper focuses on designing a convolut
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Sarkhel, Ritesh, Nibaran Das, Amit K. Saha, and Mita Nasipuri. "A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition." Pattern Recognition 58 (October 2016): 172–89. http://dx.doi.org/10.1016/j.patcog.2016.04.010.

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Adila, Nuzhat, Tabassum Fahima, and Imdadul Islam Md. "Object Detection using Convolutional Neural Network and Extended SURF with FIS." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 918–25. https://doi.org/10.35940/ijeat.E9915.069520.

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The aim of the paper is to detect object using the combination of three algorithms: convolutional neural network (CNN) and extended speeded up robust features (SUFR) and Fuzzy inference system (FIS). Here three types of objects are considered: first, we consider RGB images of hundred different types of objects (for example anchor, laptop airplane, car etc.) taken from benchmark database; second, we take grayscale images of human fingerprint from recognized database; third, Bangla handwritten alphabet from standard database. In this paper we extend the SURF algorithm then the result of the exte
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Dissertations / Theses on the topic "Handwritten Bangla digit recognition"

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Zhao, Mengqiao. "Handwritten digit recognition based on segmentation-free method." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20685.

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This thesis aims to implement a segmentation-free strategy in the context of handwritten multi-digit string recognition. Three models namely VGG-16, CRNN and 4C are built to be evaluated and benchmarked, also research about the effect of the different training set on model performance is carried out.
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Challa, Akkireddy. "Automatic Handwritten Digit Recognition On Document Images Using Machine Learning Methods." Thesis, Blekinge Tekniska Högskola, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17656.

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Context: The main purpose of this thesis is to build an automatic handwritten digit recognition method for the recognition of connected handwritten digit strings. To accomplish the recognition task, first, the digits were segmented into individual digits. Then, a digit recognition module is employed to classify each segmented digit completing the handwritten digit string recognition task. In this study, different machine learning methods, which are SVM, ANN and CNN architectures are used to achieve high performance on the digit string recognition problem. In these methods, images of digit stri
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Bailey, Alex. "Class-dependent features and multicategory classification." Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342757.

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Park, Gwang Hoon. "Handwritten digit and script recognition using density based random vector functional link network." Case Western Reserve University School of Graduate Studies / OhioLINK, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=case1061911553.

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Rogers, Spencer David. "Support Vector Machines for Classification and Imputation." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3215.

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Support vector machines (SVMs) are a powerful tool for classification problems. SVMs have only been developed in the last 20 years with the availability of cheap and abundant computing power. SVMs are a non-statistical approach and make no assumptions about the distribution of the data. Here support vector machines are applied to a classic data set from the machine learning literature and the out-of-sample misclassification rates are compared to other classification methods. Finally, an algorithm for using support vector machines to address the difficulty in imputing missing categorical data i
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Gong, Shyh-Jier, and 龔世傑. "Recognition of handwritten digit characters." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/19034124518507636417.

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碩士<br>大同工學院<br>資訊工程研究所<br>81<br>This paper presents a methodology for classifying syntactic patterns is using a feature matching against a set of proto- otypes. The prototypes are first classified and arranged into a hierarchical structure that facilitates this matching. Image of characters are described by a sequence of features extracted from the chain codes of their contours. A rotatio- nally invariant string distance measure is defined that com- pared two feature strings. The meth
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Wilder, Kenneth Joseph. "Decision tree algorithms for handwritten digit recognition." 1998. https://scholarworks.umass.edu/dissertations/AAI9823791.

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We present an original algorithm for recognizing handwritten digits. We begin by introducing a virtually infinite collection of binary geometric features. The features are queries that ask if a particular geometric arrangement of local topographic codes is present in an image. The codes, which we call "tags", are too coarse and common to be informative by themselves, but the presence of geometric arrangements of tags ("tag arrangements") can provide substantial information about the shape of an image. Tag arrangements are features that are well-suited for handwritten digit recognition as their
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Cheng, Wan-Chi, and 鄭萬旗. "Application of Neural Networks in Handwritten Digit Recognition." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/68068178227265261849.

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碩士<br>淡江大學<br>電機工程學系<br>88<br>Optical character recognition (OCR) research dates back to 1950''s. In the late 1980''s, increasing computer-power generated renewed interest in OCR for the unrestricted machine-printed characters and handwritten characters. These applications widely range from automatic mail processing, automatic data entry into large administrative systems, license-plate identification, banking, automatic cartography, to reading devices for blind. These OCR methods generally fall into three categories: statistical methods, syntactic methods, and neural-fuzzy methods. Basically
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Wang, Hao-Yu, and 王浩宇. "Handwritten English Character and Digit Recognition Using Kinect." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/48647254928685891611.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>102<br>Human-computer interaction (HCI) has been a popular research field recently. Hand gesture recognition is an important part of HCI that provides a natural way of communication. Handwritten recognition is a part of hand gesture recognition that provides an alternative method to input characters. In this thesis, we propose a handwritten recognition system to input English characters and digits without using traditional input devices such as keyboards and mice. Accuracy and real time processing are highly desired in the handwritten digit and character recognitio
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Hsieh, Tsung-Ting, and 謝宗廷. "Automatic Building Inter-digit Correlations for Handwritten Postal Code Recognition." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/70825794729357782433.

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碩士<br>佛光人文社會學院<br>資訊學系碩士班<br>94<br>We employ Recurrent Neural Networks (RNNs) to create the short term memory between digits of postal code by arranging in groups handwritten digits and image processing techniques to reach the goal of recognizing them in real time. Furthermore, we can check the correctness of input digits and predict the next one that could show up by standing on the memory. By providing the prediction, users can choose one of these digits and detect the mistake. The system can train the postal code on-line in order to create the usual combination of the three digits and forec
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Books on the topic "Handwritten Bangla digit recognition"

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

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Gattal, Abdeljalil. Segmentation-Verification for Handwritten Digit Recognition. GRIN Verlag GmbH, 2017.

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Book chapters on the topic "Handwritten Bangla digit recognition"

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Chakraborty, Partha, Syeda Surma Jahanapi, and Tanupriya Choudhury. "Bangla Handwritten Digit Recognition." In Cyber Intelligence and Information Retrieval. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4284-5_14.

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Fahim Sikder, Md. "Bangla Handwritten Digit Recognition and Generation." In Proceedings of International Joint Conference on Computational Intelligence. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7564-4_46.

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Rabby, AKM Shahariar Azad, Sheikh Abujar, Sadeka Haque, and Syed Akhter Hossain. "Bangla Handwritten Digit Recognition Using Convolutional Neural Network." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1951-8_11.

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Naim, Forhad An. "Bangla Handwritten Digit Recognition Based on Different Pixel Matrices." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2594-7_27.

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Haque, Md Reduanul, Rabeya Basri, Morium Akter, and Mohammad Shorif Uddin. "A Transfer Learning Approach for Bangla Handwritten Digit Recognition." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3741-6_3.

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Basu, Subhadip, Ram Sarkar, Nibaran Das, Mahantapas Kundu, Mita Nasipuri, and Dipak Kumar Basu. "Handwritten Bangla Digit Recognition Using Classifier Combination Through DS Technique." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11590316_32.

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Hoq, Md Nazmul, Mohammad Mohaiminul Islam, Nadira Anjum Nipa, and Md Mostofa Akbar. "A Comparative Overview of Classification Algorithm for Bangla Handwritten Digit Recognition." In Proceedings of International Joint Conference on Computational Intelligence. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7564-4_24.

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Haque, Sadeka, AKM Shahariar Azad Rabby, Md Sanzidul Islam, and Syed Akhter Hossain. "ShonkhaNet: A Dynamic Routing for Bangla Handwritten Digit Recognition Using Capsule Network." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9187-3_15.

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Shuvo, Shifat Nayme, Fuad Hasan, Mohi Uddin Ahmed, Syed Akhter Hossain, and Sheikh Abujar. "MathNET: Using CNN Bangla Handwritten Digit, Mathematical Symbols, and Trigonometric Function Recognition." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7394-1_47.

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Islam, M., S. A. Shuvo, M. S. Nipun, et al. "Efficient Approach to Using CNN-Based Pre-trained Models in Bangla Handwritten Digit Recognition." In Computational Vision and Bio-Inspired Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9819-5_50.

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Conference papers on the topic "Handwritten Bangla digit recognition"

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Ahamed, Mehedi, Radib Bin Kabir, Tawsif Tashwar Dipto, Mueeze Al Mushabbir, Sabbir Ahmed, and Md Hasanul Kabir. "Performance Analysis of Few-Shot Learning Approaches for Bangla Handwritten Character and Digit Recognition." In 2024 6th International Conference on Sustainable Technologies for Industry 5.0 (STI). IEEE, 2024. https://doi.org/10.1109/sti64222.2024.10951048.

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Haque, Md Momenul, Md Rabiul Islam, and Md Faysal Ahamed. "D3 Net: An Adam-Based Ensemble Voting Method for Bangla Handwritten Digit Recognition Using Multi-CNN Architectures." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013865.

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Ahmareen, Shafaque, Alreem Khalid Khamies Alabdouli, and Sirisha Polturi. "MNSIT Handwritten Digit Recognition using CNN." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00018.

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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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Mahmud, Md Shefat Al, Sadman Sadik Khan, Md Sadekur Rahman, Nayeem Ahmed, Nuzhat Noor Islam Prova, and Amit Kumar Gupta. "Bangla Hand-Written Digit Recognition Using Deep Learning Models." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725113.

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Hossain, M. Zahid, M. Ashraful Amin, and Hong Yan. "Rapid feature extraction for Bangla handwritten digit recognition." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6017001.

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Khan, Haider Adnan, Abdullah Al Helal, and Khawza I. Ahmed. "Handwritten Bangla digit recognition using Sparse Representation Classifier." In 2014 International Conference on Informatics, Electronics & Vision (ICIEV). IEEE, 2014. http://dx.doi.org/10.1109/iciev.2014.6850817.

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Jin-wen Xu, Jinhua Xu, and Yue Lu. "Handwritten Bangla digit recognition using hierarchical Bayesian network." In 2008 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE 2008). IEEE, 2008. http://dx.doi.org/10.1109/iske.2008.4731093.

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Boni, Pritam Khan, Bappy Shahriar Abir, H. M. Mehedi Hasan, and Md Rafiqul Islam. "Handwritten Bangla Digit Recognition Using Chemical Reaction Optimization." In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2018. http://dx.doi.org/10.1109/icccnt.2018.8494202.

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Hashem, Tahsina, Mohammad Asif, and Md Al-Amin Bhuiyan. "Handwritten Bangla digit recognition employing hybrid neural network approach." In 2013 16th International Conference on Computer and Information Technology (ICCIT). IEEE, 2014. http://dx.doi.org/10.1109/iccitechn.2014.6997353.

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