{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:IWH644XNTHAHLGF5PJAPI3B53B","short_pith_number":"pith:IWH644XN","schema_version":"1.0","canonical_sha256":"458fee72ed99c07598bd7a40f46c3dd86fa396984707fadb1b367320c734a80c","source":{"kind":"arxiv","id":"1806.00578","version":1},"attestation_state":"computed","paper":{"title":"SCAN: Sliding Convolutional Attention Network for Scene Text Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng-Lin Liu, Fei Yin, Li Liu, Xu-Yao Zhang, Yi-Chao Wu","submitted_at":"2018-06-02T03:28:43Z","abstract_excerpt":"Scene text recognition has drawn great attentions in the community of computer vision and artificial intelligence due to its challenges and wide applications. State-of-the-art recurrent neural networks (RNN) based models map an input sequence to a variable length output sequence, but are usually applied in a black box manner and lack of transparency for further improvement, and the maintaining of the entire past hidden states prevents parallel computation in a sequence. In this paper, we investigate the intrinsic characteristics of text recognition, and inspired by human cognition mechanisms i"},"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":"1806.00578","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-02T03:28:43Z","cross_cats_sorted":[],"title_canon_sha256":"21c07603b4493d4d4b475ecdb561ebb9dfcff51c044a89f78128e8320c1b6934","abstract_canon_sha256":"e9fd5900f615fcda49e86f5aec73762a9c80723e5232cddae667631230911789"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:19.021399Z","signature_b64":"6QsGgJf3e7zjugRVrlqZ6lHl7lXFvHM61MO0hUmG/nieiYTY6yu/R10ryqUgIdc1XbBkCVKXQxIgU9NzLX8NBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"458fee72ed99c07598bd7a40f46c3dd86fa396984707fadb1b367320c734a80c","last_reissued_at":"2026-05-18T00:14:19.020908Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:19.020908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SCAN: Sliding Convolutional Attention Network for Scene Text Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng-Lin Liu, Fei Yin, Li Liu, Xu-Yao Zhang, Yi-Chao Wu","submitted_at":"2018-06-02T03:28:43Z","abstract_excerpt":"Scene text recognition has drawn great attentions in the community of computer vision and artificial intelligence due to its challenges and wide applications. State-of-the-art recurrent neural networks (RNN) based models map an input sequence to a variable length output sequence, but are usually applied in a black box manner and lack of transparency for further improvement, and the maintaining of the entire past hidden states prevents parallel computation in a sequence. In this paper, we investigate the intrinsic characteristics of text recognition, and inspired by human cognition mechanisms i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00578","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":"1806.00578","created_at":"2026-05-18T00:14:19.020999+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.00578v1","created_at":"2026-05-18T00:14:19.020999+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00578","created_at":"2026-05-18T00:14:19.020999+00:00"},{"alias_kind":"pith_short_12","alias_value":"IWH644XNTHAH","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"IWH644XNTHAHLGF5","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"IWH644XN","created_at":"2026-05-18T12:32:31.084164+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.09705","citing_title":"2D-CTC for Scene Text Recognition","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B","json":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B.json","graph_json":"https://pith.science/api/pith-number/IWH644XNTHAHLGF5PJAPI3B53B/graph.json","events_json":"https://pith.science/api/pith-number/IWH644XNTHAHLGF5PJAPI3B53B/events.json","paper":"https://pith.science/paper/IWH644XN"},"agent_actions":{"view_html":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B","download_json":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B.json","view_paper":"https://pith.science/paper/IWH644XN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.00578&json=true","fetch_graph":"https://pith.science/api/pith-number/IWH644XNTHAHLGF5PJAPI3B53B/graph.json","fetch_events":"https://pith.science/api/pith-number/IWH644XNTHAHLGF5PJAPI3B53B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B/action/storage_attestation","attest_author":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B/action/author_attestation","sign_citation":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B/action/citation_signature","submit_replication":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B/action/replication_record"}},"created_at":"2026-05-18T00:14:19.020999+00:00","updated_at":"2026-05-18T00:14:19.020999+00:00"}