{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RIQSKTNEUQGI3PFGOPIKQM24VH","short_pith_number":"pith:RIQSKTNE","schema_version":"1.0","canonical_sha256":"8a21254da4a40c8dbca673d0a8335ca9c75b3ef7a8bb5c33ddfb828138ce3431","source":{"kind":"arxiv","id":"2606.24484","version":1},"attestation_state":"computed","paper":{"title":"Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Li, Chong Sun, Haojie Zhang, Jiaxin Zhang, Jing Lyu, Xingsong Ye, Yongkun Du, Zhineng Chen","submitted_at":"2026-06-23T12:18:50Z","abstract_excerpt":"WordArt (artistic text) features highly customized fonts, textures, and layouts, making WordArt-oriented scene TExt Recognition (WATER) substantially more challenging than general Scene Text Recognition (STR). Existing STR datasets and methods, typically built around regular scene text and fixed-template inputs, struggle to scale to WATER. Thus, we aim to advance this task from both data and model perspectives. On the data side, we construct a 2M synthetic dataset, WATER-S, with the scale improved by hundreds of times compared to existing artistic text data. WATER-S consists of two complementa"},"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":"2606.24484","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-23T12:18:50Z","cross_cats_sorted":[],"title_canon_sha256":"784b8cc67d4dd7b654a53791457a5fe8728f9a8ee59a0496f4453d0dec3bdeeb","abstract_canon_sha256":"d71feaab290fabd9037a1919211d84fcffdc2c669f5f7ebe53a29302291bfa8d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:15:31.894710Z","signature_b64":"WqYC7SLDZaWKbKO8UtEduiTjIe396LF2j0YW5U08leT5cVosEW8wnebenKjMLIBbfVH/+ShcH4+K1uRxd4C+Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a21254da4a40c8dbca673d0a8335ca9c75b3ef7a8bb5c33ddfb828138ce3431","last_reissued_at":"2026-06-24T01:15:31.894359Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:15:31.894359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Li, Chong Sun, Haojie Zhang, Jiaxin Zhang, Jing Lyu, Xingsong Ye, Yongkun Du, Zhineng Chen","submitted_at":"2026-06-23T12:18:50Z","abstract_excerpt":"WordArt (artistic text) features highly customized fonts, textures, and layouts, making WordArt-oriented scene TExt Recognition (WATER) substantially more challenging than general Scene Text Recognition (STR). Existing STR datasets and methods, typically built around regular scene text and fixed-template inputs, struggle to scale to WATER. Thus, we aim to advance this task from both data and model perspectives. On the data side, we construct a 2M synthetic dataset, WATER-S, with the scale improved by hundreds of times compared to existing artistic text data. WATER-S consists of two complementa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24484","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.24484/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.24484","created_at":"2026-06-24T01:15:31.894419+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.24484v1","created_at":"2026-06-24T01:15:31.894419+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24484","created_at":"2026-06-24T01:15:31.894419+00:00"},{"alias_kind":"pith_short_12","alias_value":"RIQSKTNEUQGI","created_at":"2026-06-24T01:15:31.894419+00:00"},{"alias_kind":"pith_short_16","alias_value":"RIQSKTNEUQGI3PFG","created_at":"2026-06-24T01:15:31.894419+00:00"},{"alias_kind":"pith_short_8","alias_value":"RIQSKTNE","created_at":"2026-06-24T01:15:31.894419+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/RIQSKTNEUQGI3PFGOPIKQM24VH","json":"https://pith.science/pith/RIQSKTNEUQGI3PFGOPIKQM24VH.json","graph_json":"https://pith.science/api/pith-number/RIQSKTNEUQGI3PFGOPIKQM24VH/graph.json","events_json":"https://pith.science/api/pith-number/RIQSKTNEUQGI3PFGOPIKQM24VH/events.json","paper":"https://pith.science/paper/RIQSKTNE"},"agent_actions":{"view_html":"https://pith.science/pith/RIQSKTNEUQGI3PFGOPIKQM24VH","download_json":"https://pith.science/pith/RIQSKTNEUQGI3PFGOPIKQM24VH.json","view_paper":"https://pith.science/paper/RIQSKTNE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.24484&json=true","fetch_graph":"https://pith.science/api/pith-number/RIQSKTNEUQGI3PFGOPIKQM24VH/graph.json","fetch_events":"https://pith.science/api/pith-number/RIQSKTNEUQGI3PFGOPIKQM24VH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RIQSKTNEUQGI3PFGOPIKQM24VH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RIQSKTNEUQGI3PFGOPIKQM24VH/action/storage_attestation","attest_author":"https://pith.science/pith/RIQSKTNEUQGI3PFGOPIKQM24VH/action/author_attestation","sign_citation":"https://pith.science/pith/RIQSKTNEUQGI3PFGOPIKQM24VH/action/citation_signature","submit_replication":"https://pith.science/pith/RIQSKTNEUQGI3PFGOPIKQM24VH/action/replication_record"}},"created_at":"2026-06-24T01:15:31.894419+00:00","updated_at":"2026-06-24T01:15:31.894419+00:00"}