{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SPKWQDRADT2SM2A7QZOVZY5IYD","short_pith_number":"pith:SPKWQDRA","schema_version":"1.0","canonical_sha256":"93d5680e201cf526681f865d5ce3a8c0e3bbd4dbf8b70ca870c339ab3ccd5a7a","source":{"kind":"arxiv","id":"2501.11992","version":3},"attestation_state":"computed","paper":{"title":"Survey on Hand Gesture Recognition from Visual Input","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Georgios Th. Papadopoulos, Iraklis Varlamis, Manousos Linardakis","submitted_at":"2025-01-21T09:23:22Z","abstract_excerpt":"Hand gesture recognition has become an important research area, driven by the growing demand for human-computer interaction in fields such as sign language recognition, virtual and augmented reality, and robotics. Despite the rapid growth of the field, there are few surveys that comprehensively cover recent research developments, available solutions, and benchmark datasets. This survey addresses this gap by examining the latest advancements in hand gesture and 3D hand pose recognition from various types of camera input data including RGB images, depth images, and videos from monocular or multi"},"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":"2501.11992","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-21T09:23:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"db45bdf366f039296af0d7761e72305d1da495c109362ab14ff8193fd094a78c","abstract_canon_sha256":"2991820a2bbe1f96cd1fed0120e66c8717712e1f8086338c8534027565593f2e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:45.098488Z","signature_b64":"qtE/ixOv7yH/qNIu8kAk0c7YxCaaw82HYopMEEr/PxRvXQv3TrWCWrlrMBMNIyRtxT0WihLD1aqDywN7Sb7MBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93d5680e201cf526681f865d5ce3a8c0e3bbd4dbf8b70ca870c339ab3ccd5a7a","last_reissued_at":"2026-07-05T12:03:45.097869Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:45.097869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Survey on Hand Gesture Recognition from Visual Input","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Georgios Th. Papadopoulos, Iraklis Varlamis, Manousos Linardakis","submitted_at":"2025-01-21T09:23:22Z","abstract_excerpt":"Hand gesture recognition has become an important research area, driven by the growing demand for human-computer interaction in fields such as sign language recognition, virtual and augmented reality, and robotics. Despite the rapid growth of the field, there are few surveys that comprehensively cover recent research developments, available solutions, and benchmark datasets. This survey addresses this gap by examining the latest advancements in hand gesture and 3D hand pose recognition from various types of camera input data including RGB images, depth images, and videos from monocular or multi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.11992","kind":"arxiv","version":3},"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/2501.11992/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":"2501.11992","created_at":"2026-07-05T12:03:45.097944+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.11992v3","created_at":"2026-07-05T12:03:45.097944+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.11992","created_at":"2026-07-05T12:03:45.097944+00:00"},{"alias_kind":"pith_short_12","alias_value":"SPKWQDRADT2S","created_at":"2026-07-05T12:03:45.097944+00:00"},{"alias_kind":"pith_short_16","alias_value":"SPKWQDRADT2SM2A7","created_at":"2026-07-05T12:03:45.097944+00:00"},{"alias_kind":"pith_short_8","alias_value":"SPKWQDRA","created_at":"2026-07-05T12:03:45.097944+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.09379","citing_title":"TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SPKWQDRADT2SM2A7QZOVZY5IYD","json":"https://pith.science/pith/SPKWQDRADT2SM2A7QZOVZY5IYD.json","graph_json":"https://pith.science/api/pith-number/SPKWQDRADT2SM2A7QZOVZY5IYD/graph.json","events_json":"https://pith.science/api/pith-number/SPKWQDRADT2SM2A7QZOVZY5IYD/events.json","paper":"https://pith.science/paper/SPKWQDRA"},"agent_actions":{"view_html":"https://pith.science/pith/SPKWQDRADT2SM2A7QZOVZY5IYD","download_json":"https://pith.science/pith/SPKWQDRADT2SM2A7QZOVZY5IYD.json","view_paper":"https://pith.science/paper/SPKWQDRA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.11992&json=true","fetch_graph":"https://pith.science/api/pith-number/SPKWQDRADT2SM2A7QZOVZY5IYD/graph.json","fetch_events":"https://pith.science/api/pith-number/SPKWQDRADT2SM2A7QZOVZY5IYD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SPKWQDRADT2SM2A7QZOVZY5IYD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SPKWQDRADT2SM2A7QZOVZY5IYD/action/storage_attestation","attest_author":"https://pith.science/pith/SPKWQDRADT2SM2A7QZOVZY5IYD/action/author_attestation","sign_citation":"https://pith.science/pith/SPKWQDRADT2SM2A7QZOVZY5IYD/action/citation_signature","submit_replication":"https://pith.science/pith/SPKWQDRADT2SM2A7QZOVZY5IYD/action/replication_record"}},"created_at":"2026-07-05T12:03:45.097944+00:00","updated_at":"2026-07-05T12:03:45.097944+00:00"}