{"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/effective-handwritten-digit-recognition-using","title":"Effective Handwritten Digit Recognition using Deep Convolution Neural Network","arxiv_id":null,"date":"2020-04-30","proceeding":"International Journal of Advanced Trends in Computer Science and Engineering 2020 4","authors":["Yellapragada SS Bharadwaj","Rajaram P","Sriram V.P","Sudhakar S","Kolla Bhanu Prakash"],"abstract":"This  paper  proposed  a  simple  neural  network  approach towards  handwritten  digit  recognition  using  convolution. With  machine  learning  algorithms  like  KNN, SVM/SOM, recognizing digits is considered as one of the unsolvable tasks due to its distinctiveness in the style of writing. In this paper, Convolution  Neural  Networks  are  implemented  with an MNIST  dataset  of  70000  digits  with  250  distinct forms of writings. The proposed method achieved 98.51% accuracy for real-world handwritten digit prediction with less than 0.1 % loss on training with 60000 digits while 10000 under validation.","url_abs":"https://www.researchgate.net/publication/341068954_Effective_Handwritten_Digit_Recognition_using_Deep_Convolution_Neural_Network","url_pdf":"http://www.warse.org/IJATCSE/static/pdf/file/ijatcse66922020.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":"effective-handwritten-digit-recognition-using","repo_url":"https://github.com/BharadwajYellapragada/Effective-Handwritten-Digit-Recognition-using-Deep-Convolution-Neural-Network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"handwritten-digit-recognition","task_name":"Handwritten Digit Recognition"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/handwritten-digit-recognition-on-mnist","task":"Handwritten Digit Recognition","dataset":"MNIST","model":"CNN","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"96.95%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}