{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:K2HOBALLW7RKMLQ57LH4ZZPCG6","short_pith_number":"pith:K2HOBALL","schema_version":"1.0","canonical_sha256":"568ee0816bb7e2a62e1dfacfcce5e237862bb261c82d05c93dd8c8db428a9b0e","source":{"kind":"arxiv","id":"2502.14918","version":2},"attestation_state":"computed","paper":{"title":"RAPTOR: Refined Approach for Product Table Object Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.CV","authors_text":"Aurelie Joseph, Eliott Thomas, Elodie Carel, Gaspar Deloin, Jean-Marc Ogier, Mickael Coustaty, Vincent Poulain D'Andecy","submitted_at":"2025-02-19T13:59:06Z","abstract_excerpt":"Extracting tables from documents is a critical task across various industries, especially on business documents like invoices and reports. Existing systems based on DEtection TRansformer (DETR) such as TAble TRansformer (TATR), offer solutions for Table Detection (TD) and Table Structure Recognition (TSR) but face challenges with diverse table formats and common errors like incorrect area detection and overlapping columns. This research introduces RAPTOR, a modular post-processing system designed to enhance state-of-the-art models for improved table extraction, particularly for product tables."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.14918","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-19T13:59:06Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"8d1b47dab893a35f98bb08027251ecf10f168ff3bb02dec3a607255bf1d254f7","abstract_canon_sha256":"65a76291143f21ffed2b4b909e4aff5d752206c6c62a31542b911605dddb1add"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:18:51.432729Z","signature_b64":"5MzQmHoefQz7D/rW9J5uHox8hbg9YTiaOmDCuB0PAcfA3oA/aRDFr5DYZ/ckJ5spmJod+efN1sSCHWDP/JO5Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"568ee0816bb7e2a62e1dfacfcce5e237862bb261c82d05c93dd8c8db428a9b0e","last_reissued_at":"2026-07-05T10:18:51.432239Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:18:51.432239Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RAPTOR: Refined Approach for Product Table Object Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.CV","authors_text":"Aurelie Joseph, Eliott Thomas, Elodie Carel, Gaspar Deloin, Jean-Marc Ogier, Mickael Coustaty, Vincent Poulain D'Andecy","submitted_at":"2025-02-19T13:59:06Z","abstract_excerpt":"Extracting tables from documents is a critical task across various industries, especially on business documents like invoices and reports. Existing systems based on DEtection TRansformer (DETR) such as TAble TRansformer (TATR), offer solutions for Table Detection (TD) and Table Structure Recognition (TSR) but face challenges with diverse table formats and common errors like incorrect area detection and overlapping columns. This research introduces RAPTOR, a modular post-processing system designed to enhance state-of-the-art models for improved table extraction, particularly for product tables."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14918","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.14918/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.14918","created_at":"2026-07-05T10:18:51.432294+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.14918v2","created_at":"2026-07-05T10:18:51.432294+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14918","created_at":"2026-07-05T10:18:51.432294+00:00"},{"alias_kind":"pith_short_12","alias_value":"K2HOBALLW7RK","created_at":"2026-07-05T10:18:51.432294+00:00"},{"alias_kind":"pith_short_16","alias_value":"K2HOBALLW7RKMLQ5","created_at":"2026-07-05T10:18:51.432294+00:00"},{"alias_kind":"pith_short_8","alias_value":"K2HOBALL","created_at":"2026-07-05T10:18:51.432294+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K2HOBALLW7RKMLQ57LH4ZZPCG6","json":"https://pith.science/pith/K2HOBALLW7RKMLQ57LH4ZZPCG6.json","graph_json":"https://pith.science/api/pith-number/K2HOBALLW7RKMLQ57LH4ZZPCG6/graph.json","events_json":"https://pith.science/api/pith-number/K2HOBALLW7RKMLQ57LH4ZZPCG6/events.json","paper":"https://pith.science/paper/K2HOBALL"},"agent_actions":{"view_html":"https://pith.science/pith/K2HOBALLW7RKMLQ57LH4ZZPCG6","download_json":"https://pith.science/pith/K2HOBALLW7RKMLQ57LH4ZZPCG6.json","view_paper":"https://pith.science/paper/K2HOBALL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.14918&json=true","fetch_graph":"https://pith.science/api/pith-number/K2HOBALLW7RKMLQ57LH4ZZPCG6/graph.json","fetch_events":"https://pith.science/api/pith-number/K2HOBALLW7RKMLQ57LH4ZZPCG6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K2HOBALLW7RKMLQ57LH4ZZPCG6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K2HOBALLW7RKMLQ57LH4ZZPCG6/action/storage_attestation","attest_author":"https://pith.science/pith/K2HOBALLW7RKMLQ57LH4ZZPCG6/action/author_attestation","sign_citation":"https://pith.science/pith/K2HOBALLW7RKMLQ57LH4ZZPCG6/action/citation_signature","submit_replication":"https://pith.science/pith/K2HOBALLW7RKMLQ57LH4ZZPCG6/action/replication_record"}},"created_at":"2026-07-05T10:18:51.432294+00:00","updated_at":"2026-07-05T10:18:51.432294+00:00"}