{"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/pick-processing-key-information-extraction","title":"PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks","arxiv_id":"2004.07464","date":"2020-04-16","proceeding":null,"authors":["Wenwen Yu","Ning Lu","Xianbiao Qi","Ping Gong","Rong Xiao"],"abstract":"Computer vision with state-of-the-art deep learning models has achieved huge success in the field of Optical Character Recognition (OCR) including text detection and recognition tasks recently. However, Key Information Extraction (KIE) from documents as the downstream task of OCR, having a large number of use scenarios in real-world, remains a challenge because documents not only have textual features extracting from OCR systems but also have semantic visual features that are not fully exploited and play a critical role in KIE. Too little work has been devoted to efficiently make full use of both textual and visual features of the documents. In this paper, we introduce PICK, a framework that is effective and robust in handling complex documents layout for KIE by combining graph learning with graph convolution operation, yielding a richer semantic representation containing the textual and visual features and global layout without ambiguity. Extensive experiments on real-world datasets have been conducted to show that our method outperforms baselines methods by significant margins. Our code is available at https://github.com/wenwenyu/PICK-pytorch.","url_abs":"https://arxiv.org/abs/2004.07464v3","url_pdf":"https://arxiv.org/pdf/2004.07464v3.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":"pick-processing-key-information-extraction","repo_url":"https://github.com/wenwenyu/PICK-pytorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"pick-processing-key-information-extraction","repo_url":"https://github.com/lulia0228/Document_IE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"graph-learning","task_name":"Graph Learning"},{"task_slug":"key-information-extraction","task_name":"Key Information Extraction"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"},{"task_slug":"text-detection","task_name":"Text Detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.07464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.07464"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/wenwenyu/PICK-pytorch","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/lulia0228/Document_IE","reach":null}],"summary":{"ran_draft_wrong":2,"unverified":4},"by_repo_kind":{"official":{"samples":5,"ran":1,"repositories":1},"listed":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"fac5364e2f53c6db","entry":"conv3x3","repo":"wenwenyu/PICK-pytorch","repo_kind":"official","path":"model/resnet.py","file_url":"https://github.com/wenwenyu/PICK-pytorch/blob/HEAD/model/resnet.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"code_sha256_prefix":"dea56f89237a689d","entry":"convert_loss_input","repo":"lulia0228/Document_IE","repo_kind":"listed","path":"train_lstm_gcn.py","file_url":"https://github.com/lulia0228/Document_IE/blob/HEAD/train_lstm_gcn.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dea56f89237a689d"}},{"code_sha256_prefix":"5c4456268c3d3041","entry":"read_gt_file_with_box_entity_type","repo":"wenwenyu/PICK-pytorch","repo_kind":"official","path":"data_utils/documents.py","file_url":"https://github.com/wenwenyu/PICK-pytorch/blob/HEAD/data_utils/documents.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5c4456268c3d3041"}},{"code_sha256_prefix":"888e906bd883bd33","entry":"read_ocr_file_without_box_entity_type","repo":"wenwenyu/PICK-pytorch","repo_kind":"official","path":"data_utils/documents.py","file_url":"https://github.com/wenwenyu/PICK-pytorch/blob/HEAD/data_utils/documents.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"888e906bd883bd33"}},{"code_sha256_prefix":"509f667856933fed","entry":"resnet18","repo":"wenwenyu/PICK-pytorch","repo_kind":"official","path":"model/resnet.py","file_url":"https://github.com/wenwenyu/PICK-pytorch/blob/HEAD/model/resnet.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"509f667856933fed"}},{"code_sha256_prefix":"25fd61a43ccdf31c","entry":"resnet34","repo":"wenwenyu/PICK-pytorch","repo_kind":"official","path":"model/resnet.py","file_url":"https://github.com/wenwenyu/PICK-pytorch/blob/HEAD/model/resnet.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"25fd61a43ccdf31c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}