{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ARMON4QWVV6KC3VVUYDTWSBR56","short_pith_number":"pith:ARMON4QW","schema_version":"1.0","canonical_sha256":"0458e6f216ad7ca16eb5a6073b4831ef9366170aa1f9e954400fa4afeb5845dd","source":{"kind":"arxiv","id":"1901.11382","version":1},"attestation_state":"computed","paper":{"title":"Learning to Clean: A GAN Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhishek Verma, Lovekesh Vig, Monika Sharma","submitted_at":"2019-01-28T09:50:54Z","abstract_excerpt":"In the big data era, the impetus to digitize the vast reservoirs of data trapped in unstructured scanned documents such as invoices, bank documents and courier receipts has gained fresh momentum. The scanning process often results in the introduction of artifacts such as background noise, blur due to camera motion, watermarkings, coffee stains, or faded text. These artifacts pose many readability challenges to current text recognition algorithms and significantly degrade their performance. Existing learning based denoising techniques require a dataset comprising of noisy documents paired with "},"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":"1901.11382","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-28T09:50:54Z","cross_cats_sorted":[],"title_canon_sha256":"6a21dc5d949f2ac46efa114b9fe55ae2537b99422a9055bcb6dd7670fa9952a0","abstract_canon_sha256":"56096286b69079336ce2835be90221e661ddf9908d3151ef31b7ca0b8aa4135a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:02.025924Z","signature_b64":"gPlqiyzdJltJm6yBAfvLnI4VTTI0cNKhRUXT82p0U9fuq+oSdMKRZyV9aVYhN/ITXa58kFb1mu0tfxyU7XUXBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0458e6f216ad7ca16eb5a6073b4831ef9366170aa1f9e954400fa4afeb5845dd","last_reissued_at":"2026-05-17T23:55:02.025267Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:02.025267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Clean: A GAN Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhishek Verma, Lovekesh Vig, Monika Sharma","submitted_at":"2019-01-28T09:50:54Z","abstract_excerpt":"In the big data era, the impetus to digitize the vast reservoirs of data trapped in unstructured scanned documents such as invoices, bank documents and courier receipts has gained fresh momentum. The scanning process often results in the introduction of artifacts such as background noise, blur due to camera motion, watermarkings, coffee stains, or faded text. These artifacts pose many readability challenges to current text recognition algorithms and significantly degrade their performance. Existing learning based denoising techniques require a dataset comprising of noisy documents paired with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.11382","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":"1901.11382","created_at":"2026-05-17T23:55:02.025381+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.11382v1","created_at":"2026-05-17T23:55:02.025381+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.11382","created_at":"2026-05-17T23:55:02.025381+00:00"},{"alias_kind":"pith_short_12","alias_value":"ARMON4QWVV6K","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"ARMON4QWVV6KC3VV","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"ARMON4QW","created_at":"2026-05-18T12:33:12.712433+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/ARMON4QWVV6KC3VVUYDTWSBR56","json":"https://pith.science/pith/ARMON4QWVV6KC3VVUYDTWSBR56.json","graph_json":"https://pith.science/api/pith-number/ARMON4QWVV6KC3VVUYDTWSBR56/graph.json","events_json":"https://pith.science/api/pith-number/ARMON4QWVV6KC3VVUYDTWSBR56/events.json","paper":"https://pith.science/paper/ARMON4QW"},"agent_actions":{"view_html":"https://pith.science/pith/ARMON4QWVV6KC3VVUYDTWSBR56","download_json":"https://pith.science/pith/ARMON4QWVV6KC3VVUYDTWSBR56.json","view_paper":"https://pith.science/paper/ARMON4QW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.11382&json=true","fetch_graph":"https://pith.science/api/pith-number/ARMON4QWVV6KC3VVUYDTWSBR56/graph.json","fetch_events":"https://pith.science/api/pith-number/ARMON4QWVV6KC3VVUYDTWSBR56/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ARMON4QWVV6KC3VVUYDTWSBR56/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ARMON4QWVV6KC3VVUYDTWSBR56/action/storage_attestation","attest_author":"https://pith.science/pith/ARMON4QWVV6KC3VVUYDTWSBR56/action/author_attestation","sign_citation":"https://pith.science/pith/ARMON4QWVV6KC3VVUYDTWSBR56/action/citation_signature","submit_replication":"https://pith.science/pith/ARMON4QWVV6KC3VVUYDTWSBR56/action/replication_record"}},"created_at":"2026-05-17T23:55:02.025381+00:00","updated_at":"2026-05-17T23:55:02.025381+00:00"}