{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7XZL57RM56Z2DCS66UYI6NXIW5","short_pith_number":"pith:7XZL57RM","schema_version":"1.0","canonical_sha256":"fdf2befe2cefb3a18a5ef5308f36e8b7732d3e19a2c3f4fa2683164150ccf12a","source":{"kind":"arxiv","id":"1705.06849","version":1},"attestation_state":"computed","paper":{"title":"Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lianwen Jin, Songxuan Lai, Weixin Yang","submitted_at":"2017-05-19T02:27:58Z","abstract_excerpt":"Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification. The training objective is to directly minimize intra-class variations and to push the distances between skilled forgeries and genuine samples above a given threshold. By back-propagating the training signals, our RNN network produced discriminative features with desired metrics. Additionally, we propose a novel descriptor, called the length-normalized path signature (LNPS), and apply it to online signature verific"},"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":"1705.06849","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-19T02:27:58Z","cross_cats_sorted":[],"title_canon_sha256":"cbec43c7f3a2e69b47ba368c7ef17a348078a8c51162c61be6c754d8c1764771","abstract_canon_sha256":"fc4abe07df7da1f921252da8599371d094b6847b8cb6ccb1b4579308baae9e5b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:11.149963Z","signature_b64":"66OTsN1OY9jCZa6ixhSrW9VBaO1QuJKZ5Mz3mOclCuf5174QmKuTGft9CxkOov70ibsimEH6Sk0yuEhJapzZAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fdf2befe2cefb3a18a5ef5308f36e8b7732d3e19a2c3f4fa2683164150ccf12a","last_reissued_at":"2026-05-18T00:44:11.149464Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:11.149464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lianwen Jin, Songxuan Lai, Weixin Yang","submitted_at":"2017-05-19T02:27:58Z","abstract_excerpt":"Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification. The training objective is to directly minimize intra-class variations and to push the distances between skilled forgeries and genuine samples above a given threshold. By back-propagating the training signals, our RNN network produced discriminative features with desired metrics. Additionally, we propose a novel descriptor, called the length-normalized path signature (LNPS), and apply it to online signature verific"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.06849","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":"1705.06849","created_at":"2026-05-18T00:44:11.149531+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.06849v1","created_at":"2026-05-18T00:44:11.149531+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.06849","created_at":"2026-05-18T00:44:11.149531+00:00"},{"alias_kind":"pith_short_12","alias_value":"7XZL57RM56Z2","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"7XZL57RM56Z2DCS6","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"7XZL57RM","created_at":"2026-05-18T12:31:05.417338+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/7XZL57RM56Z2DCS66UYI6NXIW5","json":"https://pith.science/pith/7XZL57RM56Z2DCS66UYI6NXIW5.json","graph_json":"https://pith.science/api/pith-number/7XZL57RM56Z2DCS66UYI6NXIW5/graph.json","events_json":"https://pith.science/api/pith-number/7XZL57RM56Z2DCS66UYI6NXIW5/events.json","paper":"https://pith.science/paper/7XZL57RM"},"agent_actions":{"view_html":"https://pith.science/pith/7XZL57RM56Z2DCS66UYI6NXIW5","download_json":"https://pith.science/pith/7XZL57RM56Z2DCS66UYI6NXIW5.json","view_paper":"https://pith.science/paper/7XZL57RM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.06849&json=true","fetch_graph":"https://pith.science/api/pith-number/7XZL57RM56Z2DCS66UYI6NXIW5/graph.json","fetch_events":"https://pith.science/api/pith-number/7XZL57RM56Z2DCS66UYI6NXIW5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7XZL57RM56Z2DCS66UYI6NXIW5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7XZL57RM56Z2DCS66UYI6NXIW5/action/storage_attestation","attest_author":"https://pith.science/pith/7XZL57RM56Z2DCS66UYI6NXIW5/action/author_attestation","sign_citation":"https://pith.science/pith/7XZL57RM56Z2DCS66UYI6NXIW5/action/citation_signature","submit_replication":"https://pith.science/pith/7XZL57RM56Z2DCS66UYI6NXIW5/action/replication_record"}},"created_at":"2026-05-18T00:44:11.149531+00:00","updated_at":"2026-05-18T00:44:11.149531+00:00"}