{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:CCQ53XMXMTGO6GS4UFUXZOQCXR","short_pith_number":"pith:CCQ53XMX","schema_version":"1.0","canonical_sha256":"10a1dddd9764ccef1a5ca1697cba02bc426c459f8d1b91356e3ae03f5e25602a","source":{"kind":"arxiv","id":"1812.00723","version":1},"attestation_state":"computed","paper":{"title":"EnsNet: Ensconce Text in the Wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lianwen Jin, Shuaitao Zhang, Songxuan Lai, Yaoxiong Huang, Yuliang Liu","submitted_at":"2018-12-03T13:25:26Z","abstract_excerpt":"A new method is proposed for removing text from natural images. The challenge is to first accurately localize text on the stroke-level and then replace it with a visually plausible background. Unlike previous methods that require image patches to erase scene text, our method, namely ensconce network (EnsNet), can operate end-to-end on a single image without any prior knowledge. The overall structure is an end-to-end trainable FCN-ResNet-18 network with a conditional generative adversarial network (cGAN). The feature of the former is first enhanced by a novel lateral connection structure and th"},"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":"1812.00723","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-03T13:25:26Z","cross_cats_sorted":[],"title_canon_sha256":"c20230f76931c5cd7a5d1113682d9c39f503d65f9a0969c2d6cc62b0c79c6705","abstract_canon_sha256":"4648ab221607edc089a48f7eab3384e5088003f0a101bdef26c0bf9df43f0e22"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:19.176540Z","signature_b64":"NnMGKEq1hmKS8zFRL0fcbePAFF02c8sKrgHnfu+lSjTmbHyYv5GtpSq+5l9swlItxGoG5LPZeX/csRlyZ54YAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10a1dddd9764ccef1a5ca1697cba02bc426c459f8d1b91356e3ae03f5e25602a","last_reissued_at":"2026-05-17T23:59:19.175942Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:19.175942Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EnsNet: Ensconce Text in the Wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lianwen Jin, Shuaitao Zhang, Songxuan Lai, Yaoxiong Huang, Yuliang Liu","submitted_at":"2018-12-03T13:25:26Z","abstract_excerpt":"A new method is proposed for removing text from natural images. The challenge is to first accurately localize text on the stroke-level and then replace it with a visually plausible background. Unlike previous methods that require image patches to erase scene text, our method, namely ensconce network (EnsNet), can operate end-to-end on a single image without any prior knowledge. The overall structure is an end-to-end trainable FCN-ResNet-18 network with a conditional generative adversarial network (cGAN). The feature of the former is first enhanced by a novel lateral connection structure and th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.00723","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":"1812.00723","created_at":"2026-05-17T23:59:19.176023+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.00723v1","created_at":"2026-05-17T23:59:19.176023+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.00723","created_at":"2026-05-17T23:59:19.176023+00:00"},{"alias_kind":"pith_short_12","alias_value":"CCQ53XMXMTGO","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"CCQ53XMXMTGO6GS4","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"CCQ53XMX","created_at":"2026-05-18T12:32:16.446611+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/CCQ53XMXMTGO6GS4UFUXZOQCXR","json":"https://pith.science/pith/CCQ53XMXMTGO6GS4UFUXZOQCXR.json","graph_json":"https://pith.science/api/pith-number/CCQ53XMXMTGO6GS4UFUXZOQCXR/graph.json","events_json":"https://pith.science/api/pith-number/CCQ53XMXMTGO6GS4UFUXZOQCXR/events.json","paper":"https://pith.science/paper/CCQ53XMX"},"agent_actions":{"view_html":"https://pith.science/pith/CCQ53XMXMTGO6GS4UFUXZOQCXR","download_json":"https://pith.science/pith/CCQ53XMXMTGO6GS4UFUXZOQCXR.json","view_paper":"https://pith.science/paper/CCQ53XMX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.00723&json=true","fetch_graph":"https://pith.science/api/pith-number/CCQ53XMXMTGO6GS4UFUXZOQCXR/graph.json","fetch_events":"https://pith.science/api/pith-number/CCQ53XMXMTGO6GS4UFUXZOQCXR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CCQ53XMXMTGO6GS4UFUXZOQCXR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CCQ53XMXMTGO6GS4UFUXZOQCXR/action/storage_attestation","attest_author":"https://pith.science/pith/CCQ53XMXMTGO6GS4UFUXZOQCXR/action/author_attestation","sign_citation":"https://pith.science/pith/CCQ53XMXMTGO6GS4UFUXZOQCXR/action/citation_signature","submit_replication":"https://pith.science/pith/CCQ53XMXMTGO6GS4UFUXZOQCXR/action/replication_record"}},"created_at":"2026-05-17T23:59:19.176023+00:00","updated_at":"2026-05-17T23:59:19.176023+00:00"}