{"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/document-image-classification-with-intra","title":"Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks","arxiv_id":"1801.09321","date":"2018-01-29","proceeding":null,"authors":["Arindam Das","Saikat Roy","Ujjwal Bhattacharya","Swapan Kumar Parui"],"abstract":"In this work, a region-based Deep Convolutional Neural Network framework is\nproposed for document structure learning. The contribution of this work\ninvolves efficient training of region based classifiers and effective\nensembling for document image classification. A primary level of `inter-domain'\ntransfer learning is used by exporting weights from a pre-trained VGG16\narchitecture on the ImageNet dataset to train a document classifier on whole\ndocument images. Exploiting the nature of region based influence modelling, a\nsecondary level of `intra-domain' transfer learning is used for rapid training\nof deep learning models for image segments. Finally, stacked generalization\nbased ensembling is utilized for combining the predictions of the base deep\nneural network models. The proposed method achieves state-of-the-art accuracy\nof 92.2% on the popular RVL-CDIP document image dataset, exceeding benchmarks\nset by existing algorithms.","url_abs":"http://arxiv.org/abs/1801.09321v3","url_pdf":"http://arxiv.org/pdf/1801.09321v3.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":"document-image-classification-with-intra","repo_url":"https://github.com/BordiaS/layoutlm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"document-image-classification-with-intra","repo_url":"https://github.com/hiarindam/document-image-classification-TL-SG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"document-image-classification-with-intra","repo_url":"https://github.com/iamarjunchandra/LayoutLM-Form-Understanding---Sequence-Labeling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"document-image-classification-with-intra","repo_url":"https://github.com/microsoft/unilm/tree/master/layoutlm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"document-image-classification","task_name":"Document Image Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"document-image-classification","task_name":"document-image-classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/document-image-classification-on-rvl-cdip","task":"Document Image Classification","dataset":"RVL-CDIP","model":"Transfer Learning from VGG16 trained on Imagenet","rank_in_archive_order":24,"of":31,"metrics":{"Accuracy":"92.21%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.09321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.09321"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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