{"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/pcgan-char-progressively-trained-classifier","title":"PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters","arxiv_id":"1908.08987","date":"2019-08-11","proceeding":null,"authors":["Qun Liu","Edward Collier","Supratik Mukhopadhyay"],"abstract":"Due to the sparsity of features, noise has proven to be a great inhibitor in the classification of handwritten characters. To combat this, most techniques perform denoising of the data before classification. In this paper, we consolidate the approach by training an all-in-one model that is able to classify even noisy characters. For classification, we progressively train a classifier generative adversarial network on the characters from low to high resolution. We show that by learning the features at each resolution independently a trained model is able to accurately classify characters even in the presence of noise. We experimentally demonstrate the effectiveness of our approach by classifying noisy versions of MNIST, handwritten Bangla Numeral, and Basic Character datasets.","url_abs":"https://arxiv.org/abs/1908.08987v1","url_pdf":"https://arxiv.org/pdf/1908.08987v1.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":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"document-image-classification","task_name":"Document Image Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/document-image-classification-on-noisy-bangla-1","task":"Document Image Classification","dataset":"Noisy Bangla Characters","model":"PCGAN-CHAR","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"89.54"},"uses_additional_data":false},{"leaderboard":"/sota/document-image-classification-on-noisy-bangla","task":"Document Image Classification","dataset":"Noisy Bangla Numeral","model":"PCGAN-CHAR","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"96.68"},"uses_additional_data":false},{"leaderboard":"/sota/document-image-classification-on-noisy-mnist-1","task":"Document Image Classification","dataset":"Noisy MNIST","model":"PCGAN-CHAR","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"98.43"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-noisy-mnist-awgn","task":"Image Classification","dataset":"Noisy MNIST (AWGN)","model":"PCGAN-CHAR","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"98.43"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-noisy-mnist-contrast","task":"Image Classification","dataset":"Noisy MNIST (Contrast)","model":"PCGAN-CHAR","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"97.25"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-noisy-mnist-motion","task":"Image Classification","dataset":"Noisy MNIST (Motion)","model":"PCGAN-CHAR","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"99.20"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}