{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IWH644XNTHAHLGF5PJAPI3B53B","short_pith_number":"pith:IWH644XN","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"},"canonical_sha256":"458fee72ed99c07598bd7a40f46c3dd86fa396984707fadb1b367320c734a80c","source":{"kind":"arxiv","id":"1806.00578","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00578","created_at":"2026-05-18T00:14:19Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00578v1","created_at":"2026-05-18T00:14:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00578","created_at":"2026-05-18T00:14:19Z"},{"alias_kind":"pith_short_12","alias_value":"IWH644XNTHAH","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IWH644XNTHAHLGF5","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IWH644XN","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IWH644XNTHAHLGF5PJAPI3B53B","target":"record","payload":{"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"},"canonical_sha256":"458fee72ed99c07598bd7a40f46c3dd86fa396984707fadb1b367320c734a80c","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"},"source_kind":"arxiv","source_id":"1806.00578","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:14:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r9bNn/eIpSmjjyH911YIx0LS4XRUfzUSv0nSCxzdr4NQp3XIIptNu22+6Bwfo+W2k5YlkYu5S9UIEgNJbReeDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T12:56:07.406047Z"},"content_sha256":"9bbcc025907c85ecd9c0f9b9e11800a3345efd2141f5bf1366e8b42a6a9288e1","schema_version":"1.0","event_id":"sha256:9bbcc025907c85ecd9c0f9b9e11800a3345efd2141f5bf1366e8b42a6a9288e1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IWH644XNTHAHLGF5PJAPI3B53B","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:14:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8I9XAaODTq9oMmRLokk407wTMpgo6IkEfjGieNIym7hDKT68Cqkk3ujQ1n8Q9nopw0WLRa6JPsF3qt0/nIs4BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T12:56:07.406741Z"},"content_sha256":"12fd79c476811c2d5eff69b1af2009440ca6a82b6fe0073a79f24c3c387a479b","schema_version":"1.0","event_id":"sha256:12fd79c476811c2d5eff69b1af2009440ca6a82b6fe0073a79f24c3c387a479b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B/bundle.json","state_url":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IWH644XNTHAHLGF5PJAPI3B53B/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T12:56:07Z","links":{"resolver":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B","bundle":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B/bundle.json","state":"https://pith.science/pith/IWH644XNTHAHLGF5PJAPI3B53B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IWH644XNTHAHLGF5PJAPI3B53B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IWH644XNTHAHLGF5PJAPI3B53B","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e9fd5900f615fcda49e86f5aec73762a9c80723e5232cddae667631230911789","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-02T03:28:43Z","title_canon_sha256":"21c07603b4493d4d4b475ecdb561ebb9dfcff51c044a89f78128e8320c1b6934"},"schema_version":"1.0","source":{"id":"1806.00578","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00578","created_at":"2026-05-18T00:14:19Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00578v1","created_at":"2026-05-18T00:14:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00578","created_at":"2026-05-18T00:14:19Z"},{"alias_kind":"pith_short_12","alias_value":"IWH644XNTHAH","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IWH644XNTHAHLGF5","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IWH644XN","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:12fd79c476811c2d5eff69b1af2009440ca6a82b6fe0073a79f24c3c387a479b","target":"graph","created_at":"2026-05-18T00:14:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"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","authors_text":"Cheng-Lin Liu, Fei Yin, Li Liu, Xu-Yao Zhang, Yi-Chao Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-02T03:28:43Z","title":"SCAN: Sliding Convolutional Attention Network for Scene Text Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00578","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9bbcc025907c85ecd9c0f9b9e11800a3345efd2141f5bf1366e8b42a6a9288e1","target":"record","created_at":"2026-05-18T00:14:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e9fd5900f615fcda49e86f5aec73762a9c80723e5232cddae667631230911789","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-02T03:28:43Z","title_canon_sha256":"21c07603b4493d4d4b475ecdb561ebb9dfcff51c044a89f78128e8320c1b6934"},"schema_version":"1.0","source":{"id":"1806.00578","kind":"arxiv","version":1}},"canonical_sha256":"458fee72ed99c07598bd7a40f46c3dd86fa396984707fadb1b367320c734a80c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"458fee72ed99c07598bd7a40f46c3dd86fa396984707fadb1b367320c734a80c","first_computed_at":"2026-05-18T00:14:19.020908Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:19.020908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6QsGgJf3e7zjugRVrlqZ6lHl7lXFvHM61MO0hUmG/nieiYTY6yu/R10ryqUgIdc1XbBkCVKXQxIgU9NzLX8NBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:19.021399Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.00578","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9bbcc025907c85ecd9c0f9b9e11800a3345efd2141f5bf1366e8b42a6a9288e1","sha256:12fd79c476811c2d5eff69b1af2009440ca6a82b6fe0073a79f24c3c387a479b"],"state_sha256":"e00458768224bb166956495c6a07ab6b6cdfb955bf8c77cc63db53735152d48d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P9Fj35uFeTjy9zNrjWbw0qaNhi9jVSfea8pYg/e1bOpjBxpuoydIay5cZnT/YPdrYbRWveyVSW1tq1xWDIKaCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T12:56:07.410822Z","bundle_sha256":"9119c2c9e62cb2782ee3f6f90c979c362cb7a5eea8c3be939890c4144a086682"}}