{"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/omnidirectional-scene-text-detection-with","title":"Omnidirectional Scene Text Detection with Sequential-free Box Discretization","arxiv_id":"1906.02371","date":"2019-06-06","proceeding":null,"authors":["Yuliang Liu","Sheng Zhang","Lianwen Jin","Lele Xie","Yaqiang Wu","Zhepeng Wang"],"abstract":"Scene text in the wild is commonly presented with high variant characteristics. Using quadrilateral bounding box to localize the text instance is nearly indispensable for detection methods. However, recent researches reveal that introducing quadrilateral bounding box for scene text detection will bring a label confusion issue which is easily overlooked, and this issue may significantly undermine the detection performance. To address this issue, in this paper, we propose a novel method called Sequential-free Box Discretization (SBD) by discretizing the bounding box into key edges (KE) which can further derive more effective methods to improve detection performance. Experiments showed that the proposed method can outperform state-of-the-art methods in many popular scene text benchmarks, including ICDAR 2015, MLT, and MSRA-TD500. Ablation study also showed that simply integrating the SBD into Mask R-CNN framework, the detection performance can be substantially improved. Furthermore, an experiment on the general object dataset HRSC2016 (multi-oriented ships) showed that our method can outperform recent state-of-the-art methods by a large margin, demonstrating its powerful generalization ability. Source code: https://github.com/Yuliang-Liu/Box_Discretization_Network.","url_abs":"https://arxiv.org/abs/1906.02371v3","url_pdf":"https://arxiv.org/pdf/1906.02371v3.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":"omnidirectional-scene-text-detection-with","repo_url":"https://github.com/Yuliang-Liu/Box_Discretization_Network","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"scene-text-detection","task_name":"Scene Text Detection"},{"task_slug":"text-detection","task_name":"Text Detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"mask-r-cnn","method_name":"Mask R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"sbd","method_name":"Spatial Broadcast Decoder"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/scene-text-detection-on-ic19-rects","task":"Scene Text Detection","dataset":"IC19-ReCTs","model":"BDN","rank_in_archive_order":1,"of":1,"metrics":{"F-Measure":"93.36"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.02371","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.02371"}},"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/Yuliang-Liu/Box_Discretization_Network","reach":null}],"summary":{"ran_draft_wrong":1,"unverified":4},"by_repo_kind":{"official":{"samples":5,"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":5,"samples":[{"code_sha256_prefix":"08d4c641043ce5c0","entry":"make_optimizer","repo":"Yuliang-Liu/Box_Discretization_Network","repo_kind":"official","path":"maskrcnn_benchmark/solver/build.py","file_url":"https://github.com/Yuliang-Liu/Box_Discretization_Network/blob/HEAD/maskrcnn_benchmark/solver/build.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"08d4c641043ce5c0"}},{"code_sha256_prefix":"a2fae60d5b59e77c","entry":"build_roi_ke_head","repo":"Yuliang-Liu/Box_Discretization_Network","repo_kind":"official","path":"maskrcnn_benchmark/modeling/roi_heads/ke_head/ke_head.py","file_url":"https://github.com/Yuliang-Liu/Box_Discretization_Network/blob/HEAD/maskrcnn_benchmark/modeling/roi_heads/ke_head/ke_head.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"a2fae60d5b59e77c"}},{"code_sha256_prefix":"772240023aa43c62","entry":"make_lr_scheduler","repo":"Yuliang-Liu/Box_Discretization_Network","repo_kind":"official","path":"maskrcnn_benchmark/solver/build.py","file_url":"https://github.com/Yuliang-Liu/Box_Discretization_Network/blob/HEAD/maskrcnn_benchmark/solver/build.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"772240023aa43c62"}},{"code_sha256_prefix":"8890777150e9a089","entry":"paraToQuad_v2","repo":"Yuliang-Liu/Box_Discretization_Network","repo_kind":"official","path":"demo/predictor.py","file_url":"https://github.com/Yuliang-Liu/Box_Discretization_Network/blob/HEAD/demo/predictor.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"8890777150e9a089"}},{"code_sha256_prefix":"60088706e0c04a55","entry":"paraToQuad_v3","repo":"Yuliang-Liu/Box_Discretization_Network","repo_kind":"official","path":"demo/predictor.py","file_url":"https://github.com/Yuliang-Liu/Box_Discretization_Network/blob/HEAD/demo/predictor.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"60088706e0c04a55"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}