{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:ER4X2XHGGJN6WM4D3LQ4ISE2Z5","short_pith_number":"pith:ER4X2XHG","canonical_record":{"source":{"id":"1908.01403","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-04T21:32:31Z","cross_cats_sorted":[],"title_canon_sha256":"153a931a4f9ad73cf766bbaa95d8206a364ed6a1ff065146d76e7c8fb5b2c7c4","abstract_canon_sha256":"76f76758fda7e29e1d85cd982d0fb2b8c875a40092b900a5a87afffed3c871df"},"schema_version":"1.0"},"canonical_sha256":"24797d5ce6325beb3383dae1c4489acf5741f8e3e102455414defe62994aa65e","source":{"kind":"arxiv","id":"1908.01403","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.01403","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"arxiv_version","alias_value":"1908.01403v3","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.01403","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_12","alias_value":"ER4X2XHGGJN6","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_16","alias_value":"ER4X2XHGGJN6WM4D","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_8","alias_value":"ER4X2XHG","created_at":"2026-07-05T00:24:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:ER4X2XHGGJN6WM4D3LQ4ISE2Z5","target":"record","payload":{"canonical_record":{"source":{"id":"1908.01403","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-04T21:32:31Z","cross_cats_sorted":[],"title_canon_sha256":"153a931a4f9ad73cf766bbaa95d8206a364ed6a1ff065146d76e7c8fb5b2c7c4","abstract_canon_sha256":"76f76758fda7e29e1d85cd982d0fb2b8c875a40092b900a5a87afffed3c871df"},"schema_version":"1.0"},"canonical_sha256":"24797d5ce6325beb3383dae1c4489acf5741f8e3e102455414defe62994aa65e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:24:55.535467Z","signature_b64":"Is9l8LiXIvc7ilKudsvDxmujvJh62Op0QhEQS5qjz0gUDt6eWqEpIOM6t+5ueHH5GHyVS6tJrQHyWU5wQdVQDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24797d5ce6325beb3383dae1c4489acf5741f8e3e102455414defe62994aa65e","last_reissued_at":"2026-07-05T00:24:55.535106Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:24:55.535106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.01403","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:24:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+npK9z5SlE9y9R/ChDNddzxxU75sVY4xDheB6bk+3DgwxEE17GAT7GFYEEdw3KYmBx1nciNCW9KORdrb2EZpAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T13:09:04.945178Z"},"content_sha256":"878c72276797aa0b6823d6b0acc43a858068d20949fe2c0dedf3420e016aea5c","schema_version":"1.0","event_id":"sha256:878c72276797aa0b6823d6b0acc43a858068d20949fe2c0dedf3420e016aea5c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:ER4X2XHGGJN6WM4D3LQ4ISE2Z5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Neural Network for Semantic-based Text Recognition in Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Margrit Betke, Qitong Wang, Yi Zheng","submitted_at":"2019-08-04T21:32:31Z","abstract_excerpt":"State-of-the-art text spotting systems typically aim to detect isolated words or word-by-word text in images of natural scenes and ignore the semantic coherence within a region of text. However, when interpreted together, seemingly isolated words may be easier to recognize. On this basis, we propose a novel \"semantic-based text recognition\" (STR) deep learning model that reads text in images with the help of understanding context. STR consists of several modules. We introduce the Text Grouping and Arranging (TGA) algorithm to connect and order isolated text regions. A text-recognition network "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.01403","kind":"arxiv","version":3},"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/1908.01403/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":"2026-07-05T00:24:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2lPFegLPZRAwrRSZUo1cIZd6XSK+oJL8MosWDBbaUbC+GVcDi/c86XHOvda6GC929GNFslW6/9i9UMZ223FTBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T13:09:04.946136Z"},"content_sha256":"23df5b852f7462bd75fe4921671d27cf31fd8aeae3ad4b77dc31ec4c849d0252","schema_version":"1.0","event_id":"sha256:23df5b852f7462bd75fe4921671d27cf31fd8aeae3ad4b77dc31ec4c849d0252"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ER4X2XHGGJN6WM4D3LQ4ISE2Z5/bundle.json","state_url":"https://pith.science/pith/ER4X2XHGGJN6WM4D3LQ4ISE2Z5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ER4X2XHGGJN6WM4D3LQ4ISE2Z5/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":"2026-08-16T13:09:04Z","links":{"resolver":"https://pith.science/pith/ER4X2XHGGJN6WM4D3LQ4ISE2Z5","bundle":"https://pith.science/pith/ER4X2XHGGJN6WM4D3LQ4ISE2Z5/bundle.json","state":"https://pith.science/pith/ER4X2XHGGJN6WM4D3LQ4ISE2Z5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ER4X2XHGGJN6WM4D3LQ4ISE2Z5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ER4X2XHGGJN6WM4D3LQ4ISE2Z5","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":"76f76758fda7e29e1d85cd982d0fb2b8c875a40092b900a5a87afffed3c871df","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-04T21:32:31Z","title_canon_sha256":"153a931a4f9ad73cf766bbaa95d8206a364ed6a1ff065146d76e7c8fb5b2c7c4"},"schema_version":"1.0","source":{"id":"1908.01403","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.01403","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"arxiv_version","alias_value":"1908.01403v3","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.01403","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_12","alias_value":"ER4X2XHGGJN6","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_16","alias_value":"ER4X2XHGGJN6WM4D","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_8","alias_value":"ER4X2XHG","created_at":"2026-07-05T00:24:55Z"}],"graph_snapshots":[{"event_id":"sha256:23df5b852f7462bd75fe4921671d27cf31fd8aeae3ad4b77dc31ec4c849d0252","target":"graph","created_at":"2026-07-05T00:24:55Z","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/1908.01403/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"State-of-the-art text spotting systems typically aim to detect isolated words or word-by-word text in images of natural scenes and ignore the semantic coherence within a region of text. However, when interpreted together, seemingly isolated words may be easier to recognize. On this basis, we propose a novel \"semantic-based text recognition\" (STR) deep learning model that reads text in images with the help of understanding context. STR consists of several modules. We introduce the Text Grouping and Arranging (TGA) algorithm to connect and order isolated text regions. A text-recognition network ","authors_text":"Margrit Betke, Qitong Wang, Yi Zheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-04T21:32:31Z","title":"Deep Neural Network for Semantic-based Text Recognition in Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.01403","kind":"arxiv","version":3},"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":"sha256:878c72276797aa0b6823d6b0acc43a858068d20949fe2c0dedf3420e016aea5c","target":"record","created_at":"2026-07-05T00:24:55Z","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":"76f76758fda7e29e1d85cd982d0fb2b8c875a40092b900a5a87afffed3c871df","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-04T21:32:31Z","title_canon_sha256":"153a931a4f9ad73cf766bbaa95d8206a364ed6a1ff065146d76e7c8fb5b2c7c4"},"schema_version":"1.0","source":{"id":"1908.01403","kind":"arxiv","version":3}},"canonical_sha256":"24797d5ce6325beb3383dae1c4489acf5741f8e3e102455414defe62994aa65e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24797d5ce6325beb3383dae1c4489acf5741f8e3e102455414defe62994aa65e","first_computed_at":"2026-07-05T00:24:55.535106Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:24:55.535106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Is9l8LiXIvc7ilKudsvDxmujvJh62Op0QhEQS5qjz0gUDt6eWqEpIOM6t+5ueHH5GHyVS6tJrQHyWU5wQdVQDA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:24:55.535467Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.01403","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:878c72276797aa0b6823d6b0acc43a858068d20949fe2c0dedf3420e016aea5c","sha256:23df5b852f7462bd75fe4921671d27cf31fd8aeae3ad4b77dc31ec4c849d0252"],"state_sha256":"5e2d30bb38f4a2da1d80723a586d3f7726bb22789866ad1aef7f7534b2528fff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XT65yI64X9SrgZnurTEH4YLnIeeRnTz8bkk42w7QiP6ZFAHRNI+iX9oGTofA1Sv1B/F4NeYwcILUmJc5zeq3AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T13:09:04.950258Z","bundle_sha256":"23f7596656358fab6b3e7ce2137aee4a38b0b11069a1a0813bbeb05b80dee44b"}}