{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:XJJWMBMS6UCEZOS6MYSMIFRZGD","short_pith_number":"pith:XJJWMBMS","schema_version":"1.0","canonical_sha256":"ba53660592f5044cba5e6624c4163930e4e4d77c39828b4134072a549b17009a","source":{"kind":"arxiv","id":"2306.15142","version":5},"attestation_state":"computed","paper":{"title":"LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jinfeng Bai, Yong Zhou, Yuchen Su, Yu-Gang Jiang, Yuning Du, Zhilong Ji, Zhineng Chen, Zhiwen Shao","submitted_at":"2023-06-27T02:03:46Z","abstract_excerpt":"Recently, regression-based methods, which predict parameterized text shapes for text localization, have gained popularity in scene text detection. However, the existing parameterized text shape methods still have limitations in modeling arbitrary-shaped texts due to ignoring the utilization of text-specific shape information. Moreover, the time consumption of the entire pipeline has been largely overlooked, leading to a suboptimal overall inference speed. To address these issues, we first propose a novel parameterized text shape method based on low-rank approximation. Unlike other shape repres"},"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":"2306.15142","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-27T02:03:46Z","cross_cats_sorted":[],"title_canon_sha256":"1dc75c44671b066c03370a4c12e531673699f946c9505a0909ad575bb6d8ee2b","abstract_canon_sha256":"9210e307c099b9395ea3f6415183ce079974156228d91122d2cdb80d2783c372"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:36:52.096134Z","signature_b64":"sEpn7du8AA/c7rciorKiQRnjDpok4Dl69LxGNKK9YJkJKiqWc30E/crEX5fhj2euLt+gc/WA/629Y//+jLi4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba53660592f5044cba5e6624c4163930e4e4d77c39828b4134072a549b17009a","last_reissued_at":"2026-07-05T07:36:52.095519Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:36:52.095519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jinfeng Bai, Yong Zhou, Yuchen Su, Yu-Gang Jiang, Yuning Du, Zhilong Ji, Zhineng Chen, Zhiwen Shao","submitted_at":"2023-06-27T02:03:46Z","abstract_excerpt":"Recently, regression-based methods, which predict parameterized text shapes for text localization, have gained popularity in scene text detection. However, the existing parameterized text shape methods still have limitations in modeling arbitrary-shaped texts due to ignoring the utilization of text-specific shape information. Moreover, the time consumption of the entire pipeline has been largely overlooked, leading to a suboptimal overall inference speed. To address these issues, we first propose a novel parameterized text shape method based on low-rank approximation. Unlike other shape repres"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.15142","kind":"arxiv","version":5},"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/2306.15142/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":"2306.15142","created_at":"2026-07-05T07:36:52.095610+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.15142v5","created_at":"2026-07-05T07:36:52.095610+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.15142","created_at":"2026-07-05T07:36:52.095610+00:00"},{"alias_kind":"pith_short_12","alias_value":"XJJWMBMS6UCE","created_at":"2026-07-05T07:36:52.095610+00:00"},{"alias_kind":"pith_short_16","alias_value":"XJJWMBMS6UCEZOS6","created_at":"2026-07-05T07:36:52.095610+00:00"},{"alias_kind":"pith_short_8","alias_value":"XJJWMBMS","created_at":"2026-07-05T07:36:52.095610+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/XJJWMBMS6UCEZOS6MYSMIFRZGD","json":"https://pith.science/pith/XJJWMBMS6UCEZOS6MYSMIFRZGD.json","graph_json":"https://pith.science/api/pith-number/XJJWMBMS6UCEZOS6MYSMIFRZGD/graph.json","events_json":"https://pith.science/api/pith-number/XJJWMBMS6UCEZOS6MYSMIFRZGD/events.json","paper":"https://pith.science/paper/XJJWMBMS"},"agent_actions":{"view_html":"https://pith.science/pith/XJJWMBMS6UCEZOS6MYSMIFRZGD","download_json":"https://pith.science/pith/XJJWMBMS6UCEZOS6MYSMIFRZGD.json","view_paper":"https://pith.science/paper/XJJWMBMS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.15142&json=true","fetch_graph":"https://pith.science/api/pith-number/XJJWMBMS6UCEZOS6MYSMIFRZGD/graph.json","fetch_events":"https://pith.science/api/pith-number/XJJWMBMS6UCEZOS6MYSMIFRZGD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XJJWMBMS6UCEZOS6MYSMIFRZGD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XJJWMBMS6UCEZOS6MYSMIFRZGD/action/storage_attestation","attest_author":"https://pith.science/pith/XJJWMBMS6UCEZOS6MYSMIFRZGD/action/author_attestation","sign_citation":"https://pith.science/pith/XJJWMBMS6UCEZOS6MYSMIFRZGD/action/citation_signature","submit_replication":"https://pith.science/pith/XJJWMBMS6UCEZOS6MYSMIFRZGD/action/replication_record"}},"created_at":"2026-07-05T07:36:52.095610+00:00","updated_at":"2026-07-05T07:36:52.095610+00:00"}