{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UTTDHCZMPNWSEGK6Q2WEGV7WSN","short_pith_number":"pith:UTTDHCZM","schema_version":"1.0","canonical_sha256":"a4e6338b2c7b6d22195e86ac4357f6934c1a22604e9ddc147586d9fb91af1a9d","source":{"kind":"arxiv","id":"2407.14224","version":2},"attestation_state":"computed","paper":{"title":"Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Arkadip Maitra, K. Kumaran, Megha Tiwari, Soumitra Samanta, Suvajit Patra, Swami Punyeshwarananda, Swathy Prabhu","submitted_at":"2024-07-19T11:48:36Z","abstract_excerpt":"Automatic Sign Language (SL) recognition is an important task in the computer vision community. To build a robust SL recognition system, we need a considerable amount of data which is lacking particularly in Indian sign language (ISL). In this paper, we introduce a large-scale isolated ISL dataset and a novel SL recognition model based on skeleton graph structure. The dataset covers 2002 daily used common words in the deaf community recorded by 20 (10 male and 10 female) deaf adult signers (contains 40033 videos). We propose a SL recognition model namely Hierarchical Windowed Graph Attention N"},"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":"2407.14224","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-19T11:48:36Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"a3949e050b3d07f3b8702ba302d3acfc949037fbc91675663da595b6def586ce","abstract_canon_sha256":"110045890b467efa758a9628e845bd6628400f044928c048907fe92303ea2571"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:12:30.150980Z","signature_b64":"gPDTmstQyu5SVRF9AG0SbCIjH3R1mkR+3HVFAkPNA2dF7AWRoAI0i4ZBGIU+mA9DeQXB4MFd5EEJIkGyZwIDDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4e6338b2c7b6d22195e86ac4357f6934c1a22604e9ddc147586d9fb91af1a9d","last_reissued_at":"2026-07-05T09:12:30.150503Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:12:30.150503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Arkadip Maitra, K. Kumaran, Megha Tiwari, Soumitra Samanta, Suvajit Patra, Swami Punyeshwarananda, Swathy Prabhu","submitted_at":"2024-07-19T11:48:36Z","abstract_excerpt":"Automatic Sign Language (SL) recognition is an important task in the computer vision community. To build a robust SL recognition system, we need a considerable amount of data which is lacking particularly in Indian sign language (ISL). In this paper, we introduce a large-scale isolated ISL dataset and a novel SL recognition model based on skeleton graph structure. The dataset covers 2002 daily used common words in the deaf community recorded by 20 (10 male and 10 female) deaf adult signers (contains 40033 videos). We propose a SL recognition model namely Hierarchical Windowed Graph Attention N"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.14224","kind":"arxiv","version":2},"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/2407.14224/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":"2407.14224","created_at":"2026-07-05T09:12:30.150557+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.14224v2","created_at":"2026-07-05T09:12:30.150557+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.14224","created_at":"2026-07-05T09:12:30.150557+00:00"},{"alias_kind":"pith_short_12","alias_value":"UTTDHCZMPNWS","created_at":"2026-07-05T09:12:30.150557+00:00"},{"alias_kind":"pith_short_16","alias_value":"UTTDHCZMPNWSEGK6","created_at":"2026-07-05T09:12:30.150557+00:00"},{"alias_kind":"pith_short_8","alias_value":"UTTDHCZM","created_at":"2026-07-05T09:12:30.150557+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2507.04465","citing_title":"Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions","ref_index":135,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UTTDHCZMPNWSEGK6Q2WEGV7WSN","json":"https://pith.science/pith/UTTDHCZMPNWSEGK6Q2WEGV7WSN.json","graph_json":"https://pith.science/api/pith-number/UTTDHCZMPNWSEGK6Q2WEGV7WSN/graph.json","events_json":"https://pith.science/api/pith-number/UTTDHCZMPNWSEGK6Q2WEGV7WSN/events.json","paper":"https://pith.science/paper/UTTDHCZM"},"agent_actions":{"view_html":"https://pith.science/pith/UTTDHCZMPNWSEGK6Q2WEGV7WSN","download_json":"https://pith.science/pith/UTTDHCZMPNWSEGK6Q2WEGV7WSN.json","view_paper":"https://pith.science/paper/UTTDHCZM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.14224&json=true","fetch_graph":"https://pith.science/api/pith-number/UTTDHCZMPNWSEGK6Q2WEGV7WSN/graph.json","fetch_events":"https://pith.science/api/pith-number/UTTDHCZMPNWSEGK6Q2WEGV7WSN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UTTDHCZMPNWSEGK6Q2WEGV7WSN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UTTDHCZMPNWSEGK6Q2WEGV7WSN/action/storage_attestation","attest_author":"https://pith.science/pith/UTTDHCZMPNWSEGK6Q2WEGV7WSN/action/author_attestation","sign_citation":"https://pith.science/pith/UTTDHCZMPNWSEGK6Q2WEGV7WSN/action/citation_signature","submit_replication":"https://pith.science/pith/UTTDHCZMPNWSEGK6Q2WEGV7WSN/action/replication_record"}},"created_at":"2026-07-05T09:12:30.150557+00:00","updated_at":"2026-07-05T09:12:30.150557+00:00"}