{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:F7LG3G726KCX2ZOQHNYZMSWTVW","short_pith_number":"pith:F7LG3G72","schema_version":"1.0","canonical_sha256":"2fd66d9bfaf2857d65d03b71964ad3ad8aa47ff6b62c1ca19c91b63b93a1485e","source":{"kind":"arxiv","id":"2002.06423","version":1},"attestation_state":"computed","paper":{"title":"Scale-Invariant Multi-Oriented Text Detection in Wild Scene Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kinjal Dasgupta, Sudip Das, Ujjwal Bhattacharya","submitted_at":"2020-02-15T18:34:15Z","abstract_excerpt":"Automatic detection of scene texts in the wild is a challenging problem, particularly due to the difficulties in handling (i) occlusions of varying percentages, (ii) widely different scales and orientations, (iii) severe degradations in the image quality etc. In this article, we propose a fully convolutional neural network architecture consisting of a novel Feature Representation Block (FRB) capable of efficient abstraction of information. The proposed network has been trained using curriculum learning with respect to difficulties in image samples and gradual pixel-wise blurring. It is capable"},"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":"2002.06423","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-15T18:34:15Z","cross_cats_sorted":[],"title_canon_sha256":"5e63478e27ccf9bd009475abbf46a4dd1bd535f3a8a56439763a8793d6bb9652","abstract_canon_sha256":"4b6f7cd35e9d972fb0f3fe3a76d76550637e78f42a25ca1503328d11c2d7eb3d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:41:06.046507Z","signature_b64":"aaa8jOaDjSOV63h/6yXcTZo+Y874eqcXvPOOPlH3fzJrmXkrtYbQX01UF+TXFXJ3r/paCAcpWzSB/0gLQqsmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2fd66d9bfaf2857d65d03b71964ad3ad8aa47ff6b62c1ca19c91b63b93a1485e","last_reissued_at":"2026-07-05T00:41:06.046090Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:41:06.046090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scale-Invariant Multi-Oriented Text Detection in Wild Scene Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kinjal Dasgupta, Sudip Das, Ujjwal Bhattacharya","submitted_at":"2020-02-15T18:34:15Z","abstract_excerpt":"Automatic detection of scene texts in the wild is a challenging problem, particularly due to the difficulties in handling (i) occlusions of varying percentages, (ii) widely different scales and orientations, (iii) severe degradations in the image quality etc. In this article, we propose a fully convolutional neural network architecture consisting of a novel Feature Representation Block (FRB) capable of efficient abstraction of information. The proposed network has been trained using curriculum learning with respect to difficulties in image samples and gradual pixel-wise blurring. It is capable"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.06423","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/2002.06423/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":"2002.06423","created_at":"2026-07-05T00:41:06.046153+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.06423v1","created_at":"2026-07-05T00:41:06.046153+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.06423","created_at":"2026-07-05T00:41:06.046153+00:00"},{"alias_kind":"pith_short_12","alias_value":"F7LG3G726KCX","created_at":"2026-07-05T00:41:06.046153+00:00"},{"alias_kind":"pith_short_16","alias_value":"F7LG3G726KCX2ZOQ","created_at":"2026-07-05T00:41:06.046153+00:00"},{"alias_kind":"pith_short_8","alias_value":"F7LG3G72","created_at":"2026-07-05T00:41:06.046153+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/F7LG3G726KCX2ZOQHNYZMSWTVW","json":"https://pith.science/pith/F7LG3G726KCX2ZOQHNYZMSWTVW.json","graph_json":"https://pith.science/api/pith-number/F7LG3G726KCX2ZOQHNYZMSWTVW/graph.json","events_json":"https://pith.science/api/pith-number/F7LG3G726KCX2ZOQHNYZMSWTVW/events.json","paper":"https://pith.science/paper/F7LG3G72"},"agent_actions":{"view_html":"https://pith.science/pith/F7LG3G726KCX2ZOQHNYZMSWTVW","download_json":"https://pith.science/pith/F7LG3G726KCX2ZOQHNYZMSWTVW.json","view_paper":"https://pith.science/paper/F7LG3G72","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.06423&json=true","fetch_graph":"https://pith.science/api/pith-number/F7LG3G726KCX2ZOQHNYZMSWTVW/graph.json","fetch_events":"https://pith.science/api/pith-number/F7LG3G726KCX2ZOQHNYZMSWTVW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F7LG3G726KCX2ZOQHNYZMSWTVW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F7LG3G726KCX2ZOQHNYZMSWTVW/action/storage_attestation","attest_author":"https://pith.science/pith/F7LG3G726KCX2ZOQHNYZMSWTVW/action/author_attestation","sign_citation":"https://pith.science/pith/F7LG3G726KCX2ZOQHNYZMSWTVW/action/citation_signature","submit_replication":"https://pith.science/pith/F7LG3G726KCX2ZOQHNYZMSWTVW/action/replication_record"}},"created_at":"2026-07-05T00:41:06.046153+00:00","updated_at":"2026-07-05T00:41:06.046153+00:00"}