{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZEOP3HK6MFRI4S5EQBISQLJAFA","short_pith_number":"pith:ZEOP3HK6","schema_version":"1.0","canonical_sha256":"c91cfd9d5e61628e4ba48051282d202815603a51bd85b3216be12976aab921f9","source":{"kind":"arxiv","id":"1907.09653","version":1},"attestation_state":"computed","paper":{"title":"GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuhui Xue, Fangneng Zhan, Shijian Lu","submitted_at":"2019-07-23T01:56:06Z","abstract_excerpt":"Recent adversarial learning research has achieved very impressive progress for modelling cross-domain data shifts in appearance space but its counterpart in modelling cross-domain shifts in geometry space lags far behind. This paper presents an innovative Geometry-Aware Domain Adaptation Network (GA-DAN) that is capable of modelling cross-domain shifts concurrently in both geometry space and appearance space and realistically converting images across domains with very different characteristics. In the proposed GA-DAN, a novel multi-modal spatial learning technique is designed which converts a "},"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":"1907.09653","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-23T01:56:06Z","cross_cats_sorted":[],"title_canon_sha256":"8bb4d037bfe2b8c2eebc2795445807850bd2eb8ec71c86e6827c439d6b5dfe7d","abstract_canon_sha256":"1995ed7b5e15f9c93fac1cc5cf3486156dea0580d06388fefdedea8d97bf78e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:50.510571Z","signature_b64":"xjoHptpH6kwhvlFNYMr1Vvv2yN918o/cMGF2DkKzNF3rlUA6NZJcpmesUvM76N33poxuacs+qb7kHbztt6R3Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c91cfd9d5e61628e4ba48051282d202815603a51bd85b3216be12976aab921f9","last_reissued_at":"2026-05-17T23:39:50.510035Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:50.510035Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuhui Xue, Fangneng Zhan, Shijian Lu","submitted_at":"2019-07-23T01:56:06Z","abstract_excerpt":"Recent adversarial learning research has achieved very impressive progress for modelling cross-domain data shifts in appearance space but its counterpart in modelling cross-domain shifts in geometry space lags far behind. This paper presents an innovative Geometry-Aware Domain Adaptation Network (GA-DAN) that is capable of modelling cross-domain shifts concurrently in both geometry space and appearance space and realistically converting images across domains with very different characteristics. In the proposed GA-DAN, a novel multi-modal spatial learning technique is designed which converts a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.09653","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":"1907.09653","created_at":"2026-05-17T23:39:50.510116+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.09653v1","created_at":"2026-05-17T23:39:50.510116+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.09653","created_at":"2026-05-17T23:39:50.510116+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZEOP3HK6MFRI","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZEOP3HK6MFRI4S5E","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZEOP3HK6","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZEOP3HK6MFRI4S5EQBISQLJAFA","json":"https://pith.science/pith/ZEOP3HK6MFRI4S5EQBISQLJAFA.json","graph_json":"https://pith.science/api/pith-number/ZEOP3HK6MFRI4S5EQBISQLJAFA/graph.json","events_json":"https://pith.science/api/pith-number/ZEOP3HK6MFRI4S5EQBISQLJAFA/events.json","paper":"https://pith.science/paper/ZEOP3HK6"},"agent_actions":{"view_html":"https://pith.science/pith/ZEOP3HK6MFRI4S5EQBISQLJAFA","download_json":"https://pith.science/pith/ZEOP3HK6MFRI4S5EQBISQLJAFA.json","view_paper":"https://pith.science/paper/ZEOP3HK6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.09653&json=true","fetch_graph":"https://pith.science/api/pith-number/ZEOP3HK6MFRI4S5EQBISQLJAFA/graph.json","fetch_events":"https://pith.science/api/pith-number/ZEOP3HK6MFRI4S5EQBISQLJAFA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZEOP3HK6MFRI4S5EQBISQLJAFA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZEOP3HK6MFRI4S5EQBISQLJAFA/action/storage_attestation","attest_author":"https://pith.science/pith/ZEOP3HK6MFRI4S5EQBISQLJAFA/action/author_attestation","sign_citation":"https://pith.science/pith/ZEOP3HK6MFRI4S5EQBISQLJAFA/action/citation_signature","submit_replication":"https://pith.science/pith/ZEOP3HK6MFRI4S5EQBISQLJAFA/action/replication_record"}},"created_at":"2026-05-17T23:39:50.510116+00:00","updated_at":"2026-05-17T23:39:50.510116+00:00"}