{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PZ3NVAZ3K2RNLEVLPK4DPQ6JXI","short_pith_number":"pith:PZ3NVAZ3","schema_version":"1.0","canonical_sha256":"7e76da833b56a2d592ab7ab837c3c9ba024322a923466f65a21a57ca790386bc","source":{"kind":"arxiv","id":"2307.05694","version":1},"attestation_state":"computed","paper":{"title":"A Survey on Figure Classification Techniques in Scientific Documents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.IR","authors_text":"Anurag Dhote, David S Doermann, Mohammed Javed","submitted_at":"2023-07-09T10:55:11Z","abstract_excerpt":"Figures visually represent an essential piece of information and provide an effective means to communicate scientific facts. Recently there have been many efforts toward extracting data directly from figures, specifically from tables, diagrams, and plots, using different Artificial Intelligence and Machine Learning techniques. This is because removing information from figures could lead to deeper insights into the concepts highlighted in the scientific documents. In this survey paper, we systematically categorize figures into five classes - tables, photos, diagrams, maps, and plots, and subseq"},"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":"2307.05694","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-07-09T10:55:11Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"f1e00361d97c49a892229efdd134d356e71552709ef9da626b6d222bf19cec8f","abstract_canon_sha256":"ea808adf849e8b998a914269c03448d72bcce412a8a24c8c6b6e92655bd9baf4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:30:11.279902Z","signature_b64":"SYkbofyD/hUmBAbI7BtETrxLzoWkBi+a8Z9+Kh9BzW2cEeGcr07d1+89wKodaNQeOqW6iK8u+bh6+rgr073ADw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e76da833b56a2d592ab7ab837c3c9ba024322a923466f65a21a57ca790386bc","last_reissued_at":"2026-07-05T06:30:11.279502Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:30:11.279502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on Figure Classification Techniques in Scientific Documents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.IR","authors_text":"Anurag Dhote, David S Doermann, Mohammed Javed","submitted_at":"2023-07-09T10:55:11Z","abstract_excerpt":"Figures visually represent an essential piece of information and provide an effective means to communicate scientific facts. Recently there have been many efforts toward extracting data directly from figures, specifically from tables, diagrams, and plots, using different Artificial Intelligence and Machine Learning techniques. This is because removing information from figures could lead to deeper insights into the concepts highlighted in the scientific documents. In this survey paper, we systematically categorize figures into five classes - tables, photos, diagrams, maps, and plots, and subseq"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.05694","kind":"arxiv","version":1},"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/2307.05694/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":"2307.05694","created_at":"2026-07-05T06:30:11.279563+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.05694v1","created_at":"2026-07-05T06:30:11.279563+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.05694","created_at":"2026-07-05T06:30:11.279563+00:00"},{"alias_kind":"pith_short_12","alias_value":"PZ3NVAZ3K2RN","created_at":"2026-07-05T06:30:11.279563+00:00"},{"alias_kind":"pith_short_16","alias_value":"PZ3NVAZ3K2RNLEVL","created_at":"2026-07-05T06:30:11.279563+00:00"},{"alias_kind":"pith_short_8","alias_value":"PZ3NVAZ3","created_at":"2026-07-05T06:30:11.279563+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.12751","citing_title":"Patent Figure Classification using Large Vision-language Models","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI","json":"https://pith.science/pith/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI.json","graph_json":"https://pith.science/api/pith-number/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI/graph.json","events_json":"https://pith.science/api/pith-number/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI/events.json","paper":"https://pith.science/paper/PZ3NVAZ3"},"agent_actions":{"view_html":"https://pith.science/pith/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI","download_json":"https://pith.science/pith/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI.json","view_paper":"https://pith.science/paper/PZ3NVAZ3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.05694&json=true","fetch_graph":"https://pith.science/api/pith-number/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI/graph.json","fetch_events":"https://pith.science/api/pith-number/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI/action/storage_attestation","attest_author":"https://pith.science/pith/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI/action/author_attestation","sign_citation":"https://pith.science/pith/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI/action/citation_signature","submit_replication":"https://pith.science/pith/PZ3NVAZ3K2RNLEVLPK4DPQ6JXI/action/replication_record"}},"created_at":"2026-07-05T06:30:11.279563+00:00","updated_at":"2026-07-05T06:30:11.279563+00:00"}