{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NEXYKBPWA46HYUPC54PSJQ4RNO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fb7b908affbec3400a237d65022fb583d636a9167530d2aa2208bfbbe3c3e7be","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-19T18:51:01Z","title_canon_sha256":"a0d48d0b92d7c080d21c49163a779163af8a7affbd87972cbce7c1e138a945cd"},"schema_version":"1.0","source":{"id":"2503.15639","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.15639","created_at":"2026-06-02T03:05:03Z"},{"alias_kind":"arxiv_version","alias_value":"2503.15639v2","created_at":"2026-06-02T03:05:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.15639","created_at":"2026-06-02T03:05:03Z"},{"alias_kind":"pith_short_12","alias_value":"NEXYKBPWA46H","created_at":"2026-06-02T03:05:03Z"},{"alias_kind":"pith_short_16","alias_value":"NEXYKBPWA46HYUPC","created_at":"2026-06-02T03:05:03Z"},{"alias_kind":"pith_short_8","alias_value":"NEXYKBPW","created_at":"2026-06-02T03:05:03Z"}],"graph_snapshots":[{"event_id":"sha256:91ed91b03c0e3b51e041cb1964d1c78dd78b0fd841c0812f79cf05ed7766399d","target":"graph","created_at":"2026-06-02T03:05:03Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.15639/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive for real-time scenarios. In such cases, the deployment of heavy models becomes impractical due to constraints on memory, computational resources, and latency. To address these challenges, we propose a novel, training-free plug-and-play framework that leverages the strengths of pre-trained text recognizers while minimizing redundant computations. Our approach uses context-based understanding and introduces an attention-based segmentation stage, whi","authors_text":"Cheng-Lin Liu, Ritabrata Chakraborty, Shivakumara Palaiahnakote, Umapada Pal","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-19T18:51:01Z","title":"A Lightweight Context-Driven Training-Free Network for Scene Text Segmentation and Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.15639","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6ba9d98bb3cd5b1dddc69c9b1e85fa66ce9973dd2dceb8ba70f2732519b86f65","target":"record","created_at":"2026-06-02T03:05:03Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"fb7b908affbec3400a237d65022fb583d636a9167530d2aa2208bfbbe3c3e7be","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-19T18:51:01Z","title_canon_sha256":"a0d48d0b92d7c080d21c49163a779163af8a7affbd87972cbce7c1e138a945cd"},"schema_version":"1.0","source":{"id":"2503.15639","kind":"arxiv","version":2}},"canonical_sha256":"692f8505f6073c7c51e2ef1f24c3916ba2b2e7d09bd134cf83076abe2c20a8c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"692f8505f6073c7c51e2ef1f24c3916ba2b2e7d09bd134cf83076abe2c20a8c6","first_computed_at":"2026-06-02T03:05:03.414264Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T03:05:03.414264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Rw+kbjAiOmVn1X7rqZEG1MGVl+5d9A842gUk/DayK0OEqIlLU2n3cQEp8eHJCZCgHHi3FiqaC92bdlas1QDBg==","signature_status":"signed_v1","signed_at":"2026-06-02T03:05:03.414689Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.15639","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6ba9d98bb3cd5b1dddc69c9b1e85fa66ce9973dd2dceb8ba70f2732519b86f65","sha256:91ed91b03c0e3b51e041cb1964d1c78dd78b0fd841c0812f79cf05ed7766399d"],"state_sha256":"41223de860854c174ba99a27c8ad8353d756f100ddd2756b97d465ba8a9250fe"}