{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WAIHCPMF6NNQNUZ6RQYC3YORES","short_pith_number":"pith:WAIHCPMF","canonical_record":{"source":{"id":"1806.00926","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-04T02:10:35Z","cross_cats_sorted":[],"title_canon_sha256":"88099ceda190f045ce36741b3e6bd7e32d489f382428c16c88dbca6192fb2d50","abstract_canon_sha256":"856bc797a264d5789aa68090538901b62eb937ec57c6cee2c2d614ffda2f41f4"},"schema_version":"1.0"},"canonical_sha256":"b010713d85f35b06d33e8c302de1d1249de5c970111d70574f67280f64e60ba2","source":{"kind":"arxiv","id":"1806.00926","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00926","created_at":"2026-07-05T00:11:02Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00926v2","created_at":"2026-07-05T00:11:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00926","created_at":"2026-07-05T00:11:02Z"},{"alias_kind":"pith_short_12","alias_value":"WAIHCPMF6NNQ","created_at":"2026-07-05T00:11:02Z"},{"alias_kind":"pith_short_16","alias_value":"WAIHCPMF6NNQNUZ6","created_at":"2026-07-05T00:11:02Z"},{"alias_kind":"pith_short_8","alias_value":"WAIHCPMF","created_at":"2026-07-05T00:11:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WAIHCPMF6NNQNUZ6RQYC3YORES","target":"record","payload":{"canonical_record":{"source":{"id":"1806.00926","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-04T02:10:35Z","cross_cats_sorted":[],"title_canon_sha256":"88099ceda190f045ce36741b3e6bd7e32d489f382428c16c88dbca6192fb2d50","abstract_canon_sha256":"856bc797a264d5789aa68090538901b62eb937ec57c6cee2c2d614ffda2f41f4"},"schema_version":"1.0"},"canonical_sha256":"b010713d85f35b06d33e8c302de1d1249de5c970111d70574f67280f64e60ba2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:11:02.574170Z","signature_b64":"hoUG8Q/an3Xm9fqOfRmSIr2ORRo6+L9T6UTjDDIODhRBBug6UzjZv8vM1i+kHW0qH2f1oxWsQRDcE7uxPSwICA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b010713d85f35b06d33e8c302de1d1249de5c970111d70574f67280f64e60ba2","last_reissued_at":"2026-07-05T00:11:02.573836Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:11:02.573836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.00926","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:11:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aGDrn8+vaMvASBZOK4/1KpGkFFpAOMpUZVy3qrqUGt2qGlaMgBt9LIkgEDSjQ3gWQgVN7Xk34cI/uDquTSp9Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T12:03:27.806862Z"},"content_sha256":"a021d37b558998d0f0d95c01ee07466dbb4ea848a7a24ba392953cf4d4de1491","schema_version":"1.0","event_id":"sha256:a021d37b558998d0f0d95c01ee07466dbb4ea848a7a24ba392953cf4d4de1491"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WAIHCPMF6NNQNUZ6RQYC3YORES","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Xu, Fenfen Sheng, Zhineng Chen","submitted_at":"2018-06-04T02:10:35Z","abstract_excerpt":"Scene text recognition has attracted a great many researches due to its importance to various applications. Existing methods mainly adopt recurrence or convolution based networks. Though have obtained good performance, these methods still suffer from two limitations: slow training speed due to the internal recurrence of RNNs, and high complexity due to stacked convolutional layers for long-term feature extraction. This paper, for the first time, proposes a no-recurrence sequence-to-sequence text recognizer, named NRTR, that dispenses with recurrences and convolutions entirely. NRTR follows the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00926","kind":"arxiv","version":2},"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/1806.00926/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:11:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NR6SaKaT2bJsm20RbTDt38Jo8avsvjXbGrODcmJZnvl9/r/i2g7PbzLhxJUYZE3nzYVI/TyEFj4BaOkqLKUbAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T12:03:27.807227Z"},"content_sha256":"d1d10ed6f0459a4991188a52d3485c5d709c5e614fa7b733297ea4f4d5cf48ae","schema_version":"1.0","event_id":"sha256:d1d10ed6f0459a4991188a52d3485c5d709c5e614fa7b733297ea4f4d5cf48ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WAIHCPMF6NNQNUZ6RQYC3YORES/bundle.json","state_url":"https://pith.science/pith/WAIHCPMF6NNQNUZ6RQYC3YORES/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WAIHCPMF6NNQNUZ6RQYC3YORES/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":"2026-07-20T12:03:27Z","links":{"resolver":"https://pith.science/pith/WAIHCPMF6NNQNUZ6RQYC3YORES","bundle":"https://pith.science/pith/WAIHCPMF6NNQNUZ6RQYC3YORES/bundle.json","state":"https://pith.science/pith/WAIHCPMF6NNQNUZ6RQYC3YORES/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WAIHCPMF6NNQNUZ6RQYC3YORES/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WAIHCPMF6NNQNUZ6RQYC3YORES","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":"856bc797a264d5789aa68090538901b62eb937ec57c6cee2c2d614ffda2f41f4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-04T02:10:35Z","title_canon_sha256":"88099ceda190f045ce36741b3e6bd7e32d489f382428c16c88dbca6192fb2d50"},"schema_version":"1.0","source":{"id":"1806.00926","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00926","created_at":"2026-07-05T00:11:02Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00926v2","created_at":"2026-07-05T00:11:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00926","created_at":"2026-07-05T00:11:02Z"},{"alias_kind":"pith_short_12","alias_value":"WAIHCPMF6NNQ","created_at":"2026-07-05T00:11:02Z"},{"alias_kind":"pith_short_16","alias_value":"WAIHCPMF6NNQNUZ6","created_at":"2026-07-05T00:11:02Z"},{"alias_kind":"pith_short_8","alias_value":"WAIHCPMF","created_at":"2026-07-05T00:11:02Z"}],"graph_snapshots":[{"event_id":"sha256:d1d10ed6f0459a4991188a52d3485c5d709c5e614fa7b733297ea4f4d5cf48ae","target":"graph","created_at":"2026-07-05T00:11:02Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1806.00926/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Scene text recognition has attracted a great many researches due to its importance to various applications. Existing methods mainly adopt recurrence or convolution based networks. Though have obtained good performance, these methods still suffer from two limitations: slow training speed due to the internal recurrence of RNNs, and high complexity due to stacked convolutional layers for long-term feature extraction. This paper, for the first time, proposes a no-recurrence sequence-to-sequence text recognizer, named NRTR, that dispenses with recurrences and convolutions entirely. NRTR follows the","authors_text":"Bo Xu, Fenfen Sheng, Zhineng Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-04T02:10:35Z","title":"NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00926","kind":"arxiv","version":2},"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:a021d37b558998d0f0d95c01ee07466dbb4ea848a7a24ba392953cf4d4de1491","target":"record","created_at":"2026-07-05T00:11:02Z","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":"856bc797a264d5789aa68090538901b62eb937ec57c6cee2c2d614ffda2f41f4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-04T02:10:35Z","title_canon_sha256":"88099ceda190f045ce36741b3e6bd7e32d489f382428c16c88dbca6192fb2d50"},"schema_version":"1.0","source":{"id":"1806.00926","kind":"arxiv","version":2}},"canonical_sha256":"b010713d85f35b06d33e8c302de1d1249de5c970111d70574f67280f64e60ba2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b010713d85f35b06d33e8c302de1d1249de5c970111d70574f67280f64e60ba2","first_computed_at":"2026-07-05T00:11:02.573836Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:11:02.573836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hoUG8Q/an3Xm9fqOfRmSIr2ORRo6+L9T6UTjDDIODhRBBug6UzjZv8vM1i+kHW0qH2f1oxWsQRDcE7uxPSwICA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:11:02.574170Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.00926","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a021d37b558998d0f0d95c01ee07466dbb4ea848a7a24ba392953cf4d4de1491","sha256:d1d10ed6f0459a4991188a52d3485c5d709c5e614fa7b733297ea4f4d5cf48ae"],"state_sha256":"9987f3a510094d89f185541d512d33442a1cd806b24c07da9325f73d90fd0cbf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DDxKqfAjZlMnMtZdpK1s57QmOX/ut0FlSnY5EK6b06rcN66C8GoGRhj0HwOaRi0l+vfskog9Xt+FuZcomMgOCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-20T12:03:27.809414Z","bundle_sha256":"2de6e1556e1bd3611a8788ec533137866f3d66d0b06ae36269a31862daebbd21"}}