{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:TWDIG535TN6F7DS2CVPDRNDJZ6","short_pith_number":"pith:TWDIG535","canonical_record":{"source":{"id":"1812.11894","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-31T16:53:21Z","cross_cats_sorted":[],"title_canon_sha256":"fa656efcdc38da38a26a654c1edf129261ed19ce6729e278e9c6bfebde2b1c5d","abstract_canon_sha256":"82aaeee390ab3fe7f5dd1154cd84f772219f8fcbccf15895ada5f2df1d0aadaf"},"schema_version":"1.0"},"canonical_sha256":"9d8683777d9b7c5f8e5a155e38b469cf954701da4b3142488081afd95d3f195a","source":{"kind":"arxiv","id":"1812.11894","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.11894","created_at":"2026-05-17T23:57:10Z"},{"alias_kind":"arxiv_version","alias_value":"1812.11894v1","created_at":"2026-05-17T23:57:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.11894","created_at":"2026-05-17T23:57:10Z"},{"alias_kind":"pith_short_12","alias_value":"TWDIG535TN6F","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"TWDIG535TN6F7DS2","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"TWDIG535","created_at":"2026-05-18T12:32:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:TWDIG535TN6F7DS2CVPDRNDJZ6","target":"record","payload":{"canonical_record":{"source":{"id":"1812.11894","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-31T16:53:21Z","cross_cats_sorted":[],"title_canon_sha256":"fa656efcdc38da38a26a654c1edf129261ed19ce6729e278e9c6bfebde2b1c5d","abstract_canon_sha256":"82aaeee390ab3fe7f5dd1154cd84f772219f8fcbccf15895ada5f2df1d0aadaf"},"schema_version":"1.0"},"canonical_sha256":"9d8683777d9b7c5f8e5a155e38b469cf954701da4b3142488081afd95d3f195a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:10.841515Z","signature_b64":"p23fD/iuEMpKuEq0AM4Fii8WjgSrCuf7Lib+10wy25K74jiZoJ/9QzeEpMWkALNiWfyjohW2D2zj1ZAbpoLNAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d8683777d9b7c5f8e5a155e38b469cf954701da4b3142488081afd95d3f195a","last_reissued_at":"2026-05-17T23:57:10.841137Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:10.841137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.11894","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":"2026-05-17T23:57:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KX+ATZdBJx0Chei5eD+mlCFyBUqzbqOaH+X/rnBjvJnp0WLf0/K0MSYt2yGbRbPkkvi70EBNv/MQgGAUmFEBDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T21:48:40.377538Z"},"content_sha256":"704745e3c7d8f3c8d4eecd773a37011662398f0495a6725ffaf047930167dd8d","schema_version":"1.0","event_id":"sha256:704745e3c7d8f3c8d4eecd773a37011662398f0495a6725ffaf047930167dd8d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:TWDIG535TN6F7DS2CVPDRNDJZ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Khaled F. Hussain, Mohamed Yousef, Usama S. Mohammed","submitted_at":"2018-12-31T16:53:21Z","abstract_excerpt":"Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.11894","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":"2026-05-17T23:57:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rG/hl365OK61HaThFibd4dCko9dJ2D45rGz6Zmval/Wb2Ve+wkQGbtJY2EALZtswEvw9HPY8VF6nbA0KteH+Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T21:48:40.378249Z"},"content_sha256":"f3c4172ddde989353df3639347e9b202f9d947767ec395c543034944f9bab64f","schema_version":"1.0","event_id":"sha256:f3c4172ddde989353df3639347e9b202f9d947767ec395c543034944f9bab64f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TWDIG535TN6F7DS2CVPDRNDJZ6/bundle.json","state_url":"https://pith.science/pith/TWDIG535TN6F7DS2CVPDRNDJZ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TWDIG535TN6F7DS2CVPDRNDJZ6/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":"2026-05-23T21:48:40Z","links":{"resolver":"https://pith.science/pith/TWDIG535TN6F7DS2CVPDRNDJZ6","bundle":"https://pith.science/pith/TWDIG535TN6F7DS2CVPDRNDJZ6/bundle.json","state":"https://pith.science/pith/TWDIG535TN6F7DS2CVPDRNDJZ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TWDIG535TN6F7DS2CVPDRNDJZ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TWDIG535TN6F7DS2CVPDRNDJZ6","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":"82aaeee390ab3fe7f5dd1154cd84f772219f8fcbccf15895ada5f2df1d0aadaf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-31T16:53:21Z","title_canon_sha256":"fa656efcdc38da38a26a654c1edf129261ed19ce6729e278e9c6bfebde2b1c5d"},"schema_version":"1.0","source":{"id":"1812.11894","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.11894","created_at":"2026-05-17T23:57:10Z"},{"alias_kind":"arxiv_version","alias_value":"1812.11894v1","created_at":"2026-05-17T23:57:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.11894","created_at":"2026-05-17T23:57:10Z"},{"alias_kind":"pith_short_12","alias_value":"TWDIG535TN6F","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"TWDIG535TN6F7DS2","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"TWDIG535","created_at":"2026-05-18T12:32:56Z"}],"graph_snapshots":[{"event_id":"sha256:f3c4172ddde989353df3639347e9b202f9d947767ec395c543034944f9bab64f","target":"graph","created_at":"2026-05-17T23:57:10Z","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":"Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition a","authors_text":"Khaled F. Hussain, Mohamed Yousef, Usama S. Mohammed","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-31T16:53:21Z","title":"Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.11894","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":"sha256:704745e3c7d8f3c8d4eecd773a37011662398f0495a6725ffaf047930167dd8d","target":"record","created_at":"2026-05-17T23:57:10Z","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":"82aaeee390ab3fe7f5dd1154cd84f772219f8fcbccf15895ada5f2df1d0aadaf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-31T16:53:21Z","title_canon_sha256":"fa656efcdc38da38a26a654c1edf129261ed19ce6729e278e9c6bfebde2b1c5d"},"schema_version":"1.0","source":{"id":"1812.11894","kind":"arxiv","version":1}},"canonical_sha256":"9d8683777d9b7c5f8e5a155e38b469cf954701da4b3142488081afd95d3f195a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9d8683777d9b7c5f8e5a155e38b469cf954701da4b3142488081afd95d3f195a","first_computed_at":"2026-05-17T23:57:10.841137Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:10.841137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p23fD/iuEMpKuEq0AM4Fii8WjgSrCuf7Lib+10wy25K74jiZoJ/9QzeEpMWkALNiWfyjohW2D2zj1ZAbpoLNAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:10.841515Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.11894","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:704745e3c7d8f3c8d4eecd773a37011662398f0495a6725ffaf047930167dd8d","sha256:f3c4172ddde989353df3639347e9b202f9d947767ec395c543034944f9bab64f"],"state_sha256":"58b04ae4d5defc02f16f3b7175dcc95dca0813d0592ffb595e36f143e3b0f23d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4KW1IDyq/3pc0rBZNGdk+cZhmNtEU+beS9v8PdjUkuaLiiwRpQ0/RELnx00YPKSKrQLiTIWuxJW4B67iv7ZjCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T21:48:40.382055Z","bundle_sha256":"e953e22a6309d76a600f14059bd3b26143f426a634478fce313c1a3489d9302d"}}