{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2DVM7G7TRBEEKTKLEZVTY4FCRR","short_pith_number":"pith:2DVM7G7T","schema_version":"1.0","canonical_sha256":"d0eacf9bf38848454d4b266b3c70a28c5a0c17c41dc46356a10421d66bab6b9e","source":{"kind":"arxiv","id":"1812.02621","version":1},"attestation_state":"computed","paper":{"title":"Hybrid Feature Learning for Handwriting Verification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jun Chu, Mihir Chauhan, Mohammad Abuzar Shaikh, Sargur Srihari","submitted_at":"2018-11-19T02:02:28Z","abstract_excerpt":"We propose an effective Hybrid Deep Learning (HDL) architecture for the task of determining the probability that a questioned handwritten word has been written by a known writer. HDL is an amalgamation of Auto-Learned Features (ALF) and Human-Engineered Features (HEF). To extract auto-learned features we use two methods: First, Two Channel Convolutional Neural Network (TC-CNN); Second, Two Channel Autoencoder (TC-AE). Furthermore, human-engineered features are extracted by using two methods: First, Gradient Structural Concavity (GSC); Second, Scale Invariant Feature Transform (SIFT). Experimen"},"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":"1812.02621","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T02:02:28Z","cross_cats_sorted":[],"title_canon_sha256":"5aa4db2dfae10f1a34499fa4a1a735b503c31b609e06e6b05dcb275350558ab3","abstract_canon_sha256":"8d4257ac0bb9cd07835bc6cbb7f4b7267c758504a3afa06d74d9c7f1af416bd9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:09:59.240522Z","signature_b64":"+F5MS5BuuKjsjwk7JxOa6oet5hFewT0drZRAnT/zzdYM9G3wJ2yARNT+8NOYzXlMS7GZAMXsL7q464+4+//7CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0eacf9bf38848454d4b266b3c70a28c5a0c17c41dc46356a10421d66bab6b9e","last_reissued_at":"2026-07-05T00:09:59.240159Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:09:59.240159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid Feature Learning for Handwriting Verification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jun Chu, Mihir Chauhan, Mohammad Abuzar Shaikh, Sargur Srihari","submitted_at":"2018-11-19T02:02:28Z","abstract_excerpt":"We propose an effective Hybrid Deep Learning (HDL) architecture for the task of determining the probability that a questioned handwritten word has been written by a known writer. HDL is an amalgamation of Auto-Learned Features (ALF) and Human-Engineered Features (HEF). To extract auto-learned features we use two methods: First, Two Channel Convolutional Neural Network (TC-CNN); Second, Two Channel Autoencoder (TC-AE). Furthermore, human-engineered features are extracted by using two methods: First, Gradient Structural Concavity (GSC); Second, Scale Invariant Feature Transform (SIFT). Experimen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.02621","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/1812.02621/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":"1812.02621","created_at":"2026-07-05T00:09:59.240215+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.02621v1","created_at":"2026-07-05T00:09:59.240215+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.02621","created_at":"2026-07-05T00:09:59.240215+00:00"},{"alias_kind":"pith_short_12","alias_value":"2DVM7G7TRBEE","created_at":"2026-07-05T00:09:59.240215+00:00"},{"alias_kind":"pith_short_16","alias_value":"2DVM7G7TRBEEKTKL","created_at":"2026-07-05T00:09:59.240215+00:00"},{"alias_kind":"pith_short_8","alias_value":"2DVM7G7T","created_at":"2026-07-05T00:09:59.240215+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/2DVM7G7TRBEEKTKLEZVTY4FCRR","json":"https://pith.science/pith/2DVM7G7TRBEEKTKLEZVTY4FCRR.json","graph_json":"https://pith.science/api/pith-number/2DVM7G7TRBEEKTKLEZVTY4FCRR/graph.json","events_json":"https://pith.science/api/pith-number/2DVM7G7TRBEEKTKLEZVTY4FCRR/events.json","paper":"https://pith.science/paper/2DVM7G7T"},"agent_actions":{"view_html":"https://pith.science/pith/2DVM7G7TRBEEKTKLEZVTY4FCRR","download_json":"https://pith.science/pith/2DVM7G7TRBEEKTKLEZVTY4FCRR.json","view_paper":"https://pith.science/paper/2DVM7G7T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.02621&json=true","fetch_graph":"https://pith.science/api/pith-number/2DVM7G7TRBEEKTKLEZVTY4FCRR/graph.json","fetch_events":"https://pith.science/api/pith-number/2DVM7G7TRBEEKTKLEZVTY4FCRR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2DVM7G7TRBEEKTKLEZVTY4FCRR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2DVM7G7TRBEEKTKLEZVTY4FCRR/action/storage_attestation","attest_author":"https://pith.science/pith/2DVM7G7TRBEEKTKLEZVTY4FCRR/action/author_attestation","sign_citation":"https://pith.science/pith/2DVM7G7TRBEEKTKLEZVTY4FCRR/action/citation_signature","submit_replication":"https://pith.science/pith/2DVM7G7TRBEEKTKLEZVTY4FCRR/action/replication_record"}},"created_at":"2026-07-05T00:09:59.240215+00:00","updated_at":"2026-07-05T00:09:59.240215+00:00"}