{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:IKLG67UITWIUJGOTYEGMEAV5A3","short_pith_number":"pith:IKLG67UI","schema_version":"1.0","canonical_sha256":"42966f7e889d914499d3c10cc202bd06c90509185e74b22aceb152852592ef99","source":{"kind":"arxiv","id":"1905.08633","version":1},"attestation_state":"computed","paper":{"title":"Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Dian Ang Yap, John Whaley, Mihail Douhaniaris, Preethi Seshadri, Sanghyun Han, Vinay Uday Prabhu","submitted_at":"2019-05-16T20:38:05Z","abstract_excerpt":"In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets. This seed dataset of images is then augmented to create a purely synthetic training dataset, which is in turn used to train a deep neural network and test on held-out real world handwritten digits dataset spanning five Indic scripts, Kannada, Tamil, Gujarati, Malayalam, and Devanagari. We showcase the efficacy of this approach both qualitatively, by training a Bounda"},"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":"1905.08633","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-16T20:38:05Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"c7d6600c33d3e6c860c9af348ffa59ed03c8e0f986d6071b7d3e3f0a22ad67d1","abstract_canon_sha256":"0b144fd15f5422df58f0c030d2100abb9d057bc6a0c0fdde6c108f220fdbc9bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:42.586255Z","signature_b64":"mQTcVGCJ0VGinwUuw0tWoCggD6ENj7Y8vD8+kY6UZS+omCTtjqf58YDrWNb2Y1/fbBXj9yUJaQy5qf25MyVeBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42966f7e889d914499d3c10cc202bd06c90509185e74b22aceb152852592ef99","last_reissued_at":"2026-05-17T23:45:42.585636Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:42.585636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Dian Ang Yap, John Whaley, Mihail Douhaniaris, Preethi Seshadri, Sanghyun Han, Vinay Uday Prabhu","submitted_at":"2019-05-16T20:38:05Z","abstract_excerpt":"In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets. This seed dataset of images is then augmented to create a purely synthetic training dataset, which is in turn used to train a deep neural network and test on held-out real world handwritten digits dataset spanning five Indic scripts, Kannada, Tamil, Gujarati, Malayalam, and Devanagari. We showcase the efficacy of this approach both qualitatively, by training a Bounda"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.08633","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":""},"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":"1905.08633","created_at":"2026-05-17T23:45:42.585727+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.08633v1","created_at":"2026-05-17T23:45:42.585727+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.08633","created_at":"2026-05-17T23:45:42.585727+00:00"},{"alias_kind":"pith_short_12","alias_value":"IKLG67UITWIU","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"IKLG67UITWIUJGOT","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"IKLG67UI","created_at":"2026-05-18T12:33:18.533446+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/IKLG67UITWIUJGOTYEGMEAV5A3","json":"https://pith.science/pith/IKLG67UITWIUJGOTYEGMEAV5A3.json","graph_json":"https://pith.science/api/pith-number/IKLG67UITWIUJGOTYEGMEAV5A3/graph.json","events_json":"https://pith.science/api/pith-number/IKLG67UITWIUJGOTYEGMEAV5A3/events.json","paper":"https://pith.science/paper/IKLG67UI"},"agent_actions":{"view_html":"https://pith.science/pith/IKLG67UITWIUJGOTYEGMEAV5A3","download_json":"https://pith.science/pith/IKLG67UITWIUJGOTYEGMEAV5A3.json","view_paper":"https://pith.science/paper/IKLG67UI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.08633&json=true","fetch_graph":"https://pith.science/api/pith-number/IKLG67UITWIUJGOTYEGMEAV5A3/graph.json","fetch_events":"https://pith.science/api/pith-number/IKLG67UITWIUJGOTYEGMEAV5A3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IKLG67UITWIUJGOTYEGMEAV5A3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IKLG67UITWIUJGOTYEGMEAV5A3/action/storage_attestation","attest_author":"https://pith.science/pith/IKLG67UITWIUJGOTYEGMEAV5A3/action/author_attestation","sign_citation":"https://pith.science/pith/IKLG67UITWIUJGOTYEGMEAV5A3/action/citation_signature","submit_replication":"https://pith.science/pith/IKLG67UITWIUJGOTYEGMEAV5A3/action/replication_record"}},"created_at":"2026-05-17T23:45:42.585727+00:00","updated_at":"2026-05-17T23:45:42.585727+00:00"}