{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:PSY7NLNWALP35VIOLYRLAUE5VX","short_pith_number":"pith:PSY7NLNW","schema_version":"1.0","canonical_sha256":"7cb1f6adb602dfbed50e5e22b0509dadf78c8e0ab6f398417f909d6fe8a589eb","source":{"kind":"arxiv","id":"1705.05787","version":1},"attestation_state":"computed","paper":{"title":"Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luiz G. Hafemann, Luiz S. Oliveira, Robert Sabourin","submitted_at":"2017-05-16T16:08:09Z","abstract_excerpt":"Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person's signature and deliberately attempt to imitate it. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries. This reflects in a relatively poor performance, with verification errors around 7% in the best systems in the literature. To address both the difficulty of obtaining "},"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":"1705.05787","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-16T16:08:09Z","cross_cats_sorted":[],"title_canon_sha256":"66a295cd8b29a70cc6fb62c08a235a89a86cfc6de466673103c953fb9729f764","abstract_canon_sha256":"4c310eeade3b6817f6f9f138338d9d354313e4bd13696012605d8077f10d717b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:20.163225Z","signature_b64":"fn7UpR5AwAuMmtRDR0LHnulVnAleYaBXmW1O8UZY5YBeWpspM8QYe0IwRECM2n8eKsk9pI3/m56y5eecfGbsBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cb1f6adb602dfbed50e5e22b0509dadf78c8e0ab6f398417f909d6fe8a589eb","last_reissued_at":"2026-05-18T00:44:20.162838Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:20.162838Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luiz G. Hafemann, Luiz S. Oliveira, Robert Sabourin","submitted_at":"2017-05-16T16:08:09Z","abstract_excerpt":"Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person's signature and deliberately attempt to imitate it. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries. This reflects in a relatively poor performance, with verification errors around 7% in the best systems in the literature. To address both the difficulty of obtaining "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.05787","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":""},"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":"1705.05787","created_at":"2026-05-18T00:44:20.162897+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.05787v1","created_at":"2026-05-18T00:44:20.162897+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.05787","created_at":"2026-05-18T00:44:20.162897+00:00"},{"alias_kind":"pith_short_12","alias_value":"PSY7NLNWALP3","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"PSY7NLNWALP35VIO","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"PSY7NLNW","created_at":"2026-05-18T12:31:37.085036+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/PSY7NLNWALP35VIOLYRLAUE5VX","json":"https://pith.science/pith/PSY7NLNWALP35VIOLYRLAUE5VX.json","graph_json":"https://pith.science/api/pith-number/PSY7NLNWALP35VIOLYRLAUE5VX/graph.json","events_json":"https://pith.science/api/pith-number/PSY7NLNWALP35VIOLYRLAUE5VX/events.json","paper":"https://pith.science/paper/PSY7NLNW"},"agent_actions":{"view_html":"https://pith.science/pith/PSY7NLNWALP35VIOLYRLAUE5VX","download_json":"https://pith.science/pith/PSY7NLNWALP35VIOLYRLAUE5VX.json","view_paper":"https://pith.science/paper/PSY7NLNW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.05787&json=true","fetch_graph":"https://pith.science/api/pith-number/PSY7NLNWALP35VIOLYRLAUE5VX/graph.json","fetch_events":"https://pith.science/api/pith-number/PSY7NLNWALP35VIOLYRLAUE5VX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PSY7NLNWALP35VIOLYRLAUE5VX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PSY7NLNWALP35VIOLYRLAUE5VX/action/storage_attestation","attest_author":"https://pith.science/pith/PSY7NLNWALP35VIOLYRLAUE5VX/action/author_attestation","sign_citation":"https://pith.science/pith/PSY7NLNWALP35VIOLYRLAUE5VX/action/citation_signature","submit_replication":"https://pith.science/pith/PSY7NLNWALP35VIOLYRLAUE5VX/action/replication_record"}},"created_at":"2026-05-18T00:44:20.162897+00:00","updated_at":"2026-05-18T00:44:20.162897+00:00"}