{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/akhcrnet-bengali-handwritten-character","title":"AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning","arxiv_id":"2008.12995","date":"2020-08-29","proceeding":null,"authors":["Akash Roy"],"abstract":"I propose a state of the art deep neural architectural solution for handwritten character recognition for Bengali alphabets, compound characters as well as numerical digits that achieves state-of-the-art accuracy 96.8% in just 11 epochs. Similar work has been done before by Chatterjee, Swagato, et al. but they achieved 96.12% accuracy in about 47 epochs. The deep neural architecture used in that paper was fairly large considering the inclusion of the weights of the ResNet 50 model which is a 50 layer Residual Network. This proposed model achieves higher accuracy as compared to any previous work & in a little number of epochs. ResNet50 is a good model trained on the ImageNet dataset, but I propose an HCR network that is trained from the scratch on Bengali characters without the \"Ensemble Learning\" that can outperform previous architectures.","url_abs":"https://arxiv.org/abs/2008.12995v3","url_pdf":"https://arxiv.org/pdf/2008.12995v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"akhcrnet-bengali-handwritten-character","repo_url":"https://github.com/theroyakash/AKHCRNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"handwriting-recognition","task_name":"Handwriting Recognition"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/handwriting-recognition-on-banglalekha","task":"Handwriting Recognition","dataset":"BanglaLekha Isolated Dataset","model":"AKHCRNet","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"96.8","Cross Entropy Loss":"0.21612","Epochs":"11"},"uses_additional_data":false},{"leaderboard":"/sota/transfer-learning-on-banglalekha-isolated","task":"Transfer Learning","dataset":"BanglaLekha Isolated Dataset","model":"Chatterjee, Dutta et al.[1]","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"96.12"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}