{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:XNCHZ6D5UUHR6VS4L5TYVNLLR4","short_pith_number":"pith:XNCHZ6D5","schema_version":"1.0","canonical_sha256":"bb447cf87da50f1f565c5f678ab56b8f330eee8f5593f16b9aea452ff7e62359","source":{"kind":"arxiv","id":"1709.01727","version":1},"attestation_state":"computed","paper":{"title":"Scene Text Recognition with Sliding Convolutional Character Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng-Lin Liu, Fei Yin, Xu-Yao Zhang, Yi-Chao Wu","submitted_at":"2017-09-06T09:01:53Z","abstract_excerpt":"Scene text recognition has attracted great interests from the computer vision and pattern recognition community in recent years. State-of-the-art methods use concolutional neural networks (CNNs), recurrent neural networks with long short-term memory (RNN-LSTM) or the combination of them. In this paper, we investigate the intrinsic characteristics of text recognition, and inspired by human cognition mechanisms in reading texts, we propose a scene text recognition method with character models on convolutional feature map. The method simultaneously detects and recognizes characters by sliding the"},"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":"1709.01727","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-06T09:01:53Z","cross_cats_sorted":[],"title_canon_sha256":"157d23e51757d4aa23bfee433b9102b0ce298c696de6e1b7de877b053a360474","abstract_canon_sha256":"2f58510650243099f280376208a83a369b6dae867e7d75c9bf6c5bb90deabd15"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:54.487798Z","signature_b64":"Fz2dE//dVE+sboOxt+sWVz+Mg+94amAtOhHz4BPzAbYfWSwO5cMhu2pkVxd8Ae7QN+YQPj+giFbBTCpW6FWDAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb447cf87da50f1f565c5f678ab56b8f330eee8f5593f16b9aea452ff7e62359","last_reissued_at":"2026-05-18T00:35:54.487304Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:54.487304Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scene Text Recognition with Sliding Convolutional Character Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng-Lin Liu, Fei Yin, Xu-Yao Zhang, Yi-Chao Wu","submitted_at":"2017-09-06T09:01:53Z","abstract_excerpt":"Scene text recognition has attracted great interests from the computer vision and pattern recognition community in recent years. State-of-the-art methods use concolutional neural networks (CNNs), recurrent neural networks with long short-term memory (RNN-LSTM) or the combination of them. In this paper, we investigate the intrinsic characteristics of text recognition, and inspired by human cognition mechanisms in reading texts, we propose a scene text recognition method with character models on convolutional feature map. The method simultaneously detects and recognizes characters by sliding the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01727","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":"1709.01727","created_at":"2026-05-18T00:35:54.487388+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.01727v1","created_at":"2026-05-18T00:35:54.487388+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.01727","created_at":"2026-05-18T00:35:54.487388+00:00"},{"alias_kind":"pith_short_12","alias_value":"XNCHZ6D5UUHR","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"XNCHZ6D5UUHR6VS4","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"XNCHZ6D5","created_at":"2026-05-18T12:31:56.362134+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/XNCHZ6D5UUHR6VS4L5TYVNLLR4","json":"https://pith.science/pith/XNCHZ6D5UUHR6VS4L5TYVNLLR4.json","graph_json":"https://pith.science/api/pith-number/XNCHZ6D5UUHR6VS4L5TYVNLLR4/graph.json","events_json":"https://pith.science/api/pith-number/XNCHZ6D5UUHR6VS4L5TYVNLLR4/events.json","paper":"https://pith.science/paper/XNCHZ6D5"},"agent_actions":{"view_html":"https://pith.science/pith/XNCHZ6D5UUHR6VS4L5TYVNLLR4","download_json":"https://pith.science/pith/XNCHZ6D5UUHR6VS4L5TYVNLLR4.json","view_paper":"https://pith.science/paper/XNCHZ6D5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.01727&json=true","fetch_graph":"https://pith.science/api/pith-number/XNCHZ6D5UUHR6VS4L5TYVNLLR4/graph.json","fetch_events":"https://pith.science/api/pith-number/XNCHZ6D5UUHR6VS4L5TYVNLLR4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XNCHZ6D5UUHR6VS4L5TYVNLLR4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XNCHZ6D5UUHR6VS4L5TYVNLLR4/action/storage_attestation","attest_author":"https://pith.science/pith/XNCHZ6D5UUHR6VS4L5TYVNLLR4/action/author_attestation","sign_citation":"https://pith.science/pith/XNCHZ6D5UUHR6VS4L5TYVNLLR4/action/citation_signature","submit_replication":"https://pith.science/pith/XNCHZ6D5UUHR6VS4L5TYVNLLR4/action/replication_record"}},"created_at":"2026-05-18T00:35:54.487388+00:00","updated_at":"2026-05-18T00:35:54.487388+00:00"}