{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:3JVGONFF2BSH6CICFOP6IKPIIP","short_pith_number":"pith:3JVGONFF","canonical_record":{"source":{"id":"1405.5248","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-20T22:02:54Z","cross_cats_sorted":[],"title_canon_sha256":"1467e89e5648be0a1d34c2ab3bb6a092a0b443256b659ec7a16a3ee9b1bc994b","abstract_canon_sha256":"ec3383ef7180ab66d586992ad2268d1c47b352b5656706d43fab63da582ab5d5"},"schema_version":"1.0"},"canonical_sha256":"da6a6734a5d0647f09022b9fe429e843cc15e10e5d472d61416d631ede1426d1","source":{"kind":"arxiv","id":"1405.5248","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.5248","created_at":"2026-05-18T02:51:24Z"},{"alias_kind":"arxiv_version","alias_value":"1405.5248v1","created_at":"2026-05-18T02:51:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.5248","created_at":"2026-05-18T02:51:24Z"},{"alias_kind":"pith_short_12","alias_value":"3JVGONFF2BSH","created_at":"2026-05-18T12:28:11Z"},{"alias_kind":"pith_short_16","alias_value":"3JVGONFF2BSH6CIC","created_at":"2026-05-18T12:28:11Z"},{"alias_kind":"pith_short_8","alias_value":"3JVGONFF","created_at":"2026-05-18T12:28:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:3JVGONFF2BSH6CICFOP6IKPIIP","target":"record","payload":{"canonical_record":{"source":{"id":"1405.5248","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-20T22:02:54Z","cross_cats_sorted":[],"title_canon_sha256":"1467e89e5648be0a1d34c2ab3bb6a092a0b443256b659ec7a16a3ee9b1bc994b","abstract_canon_sha256":"ec3383ef7180ab66d586992ad2268d1c47b352b5656706d43fab63da582ab5d5"},"schema_version":"1.0"},"canonical_sha256":"da6a6734a5d0647f09022b9fe429e843cc15e10e5d472d61416d631ede1426d1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:51:24.702276Z","signature_b64":"as6Vpuqp+OgV8IaOEtEXh5EyC9GVlAbJR+BXNb8C4nayRiz6WeVVbrUX/HDo00NlUc7/lhyr0GTPnz+NzSV7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da6a6734a5d0647f09022b9fe429e843cc15e10e5d472d61416d631ede1426d1","last_reissued_at":"2026-05-18T02:51:24.701660Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:51:24.701660Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1405.5248","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:51:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/A3YJkTlll1KEdfPlIbS5wzIraYPze+XpySf6VOFT5m8/PI/Yi/r08DVQzOK3IPwKGhlIs45uZKHZqxaKSEXAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T14:14:36.962937Z"},"content_sha256":"58d61cff6e16b9218036e565e6bbbf463771a8a515ba32cc2b11617e6a6ab92e","schema_version":"1.0","event_id":"sha256:58d61cff6e16b9218036e565e6bbbf463771a8a515ba32cc2b11617e6a6ab92e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:3JVGONFF2BSH6CICFOP6IKPIIP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Khaoula jayech, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara, Nesrine Trimech","submitted_at":"2014-05-20T22:02:54Z","abstract_excerpt":"This paper presents a new probabilistic graphical model used to model and recognize words representing the names of Tunisian cities. In fact, this work is based on a dynamic hierarchical Bayesian network. The aim is to find the best model of Arabic handwriting to reduce the complexity of the recognition process by permitting the partial recognition. Actually, we propose a segmentation of the word based on smoothing the vertical histogram projection using different width values to reduce the error of segmentation. Then, we extract the characteristics of each cell using the Zernike and HU moment"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.5248","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:51:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WlvnMZv/R+SmtoO6RHRjigORmYh8v4TMTcmFPpcnTB5YWEB4ETE0BasZuQf9T9znXAR4y5ot+7D914cWEPiLDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T14:14:36.963278Z"},"content_sha256":"045f59cb9f8dc1b656fb8a791d0f2d51b6c829d64f61e08942794e14c688f2b8","schema_version":"1.0","event_id":"sha256:045f59cb9f8dc1b656fb8a791d0f2d51b6c829d64f61e08942794e14c688f2b8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3JVGONFF2BSH6CICFOP6IKPIIP/bundle.json","state_url":"https://pith.science/pith/3JVGONFF2BSH6CICFOP6IKPIIP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3JVGONFF2BSH6CICFOP6IKPIIP/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-03T14:14:36Z","links":{"resolver":"https://pith.science/pith/3JVGONFF2BSH6CICFOP6IKPIIP","bundle":"https://pith.science/pith/3JVGONFF2BSH6CICFOP6IKPIIP/bundle.json","state":"https://pith.science/pith/3JVGONFF2BSH6CICFOP6IKPIIP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3JVGONFF2BSH6CICFOP6IKPIIP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:3JVGONFF2BSH6CICFOP6IKPIIP","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":"ec3383ef7180ab66d586992ad2268d1c47b352b5656706d43fab63da582ab5d5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-20T22:02:54Z","title_canon_sha256":"1467e89e5648be0a1d34c2ab3bb6a092a0b443256b659ec7a16a3ee9b1bc994b"},"schema_version":"1.0","source":{"id":"1405.5248","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.5248","created_at":"2026-05-18T02:51:24Z"},{"alias_kind":"arxiv_version","alias_value":"1405.5248v1","created_at":"2026-05-18T02:51:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.5248","created_at":"2026-05-18T02:51:24Z"},{"alias_kind":"pith_short_12","alias_value":"3JVGONFF2BSH","created_at":"2026-05-18T12:28:11Z"},{"alias_kind":"pith_short_16","alias_value":"3JVGONFF2BSH6CIC","created_at":"2026-05-18T12:28:11Z"},{"alias_kind":"pith_short_8","alias_value":"3JVGONFF","created_at":"2026-05-18T12:28:11Z"}],"graph_snapshots":[{"event_id":"sha256:045f59cb9f8dc1b656fb8a791d0f2d51b6c829d64f61e08942794e14c688f2b8","target":"graph","created_at":"2026-05-18T02:51:24Z","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"},"paper":{"abstract_excerpt":"This paper presents a new probabilistic graphical model used to model and recognize words representing the names of Tunisian cities. In fact, this work is based on a dynamic hierarchical Bayesian network. The aim is to find the best model of Arabic handwriting to reduce the complexity of the recognition process by permitting the partial recognition. Actually, we propose a segmentation of the word based on smoothing the vertical histogram projection using different width values to reduce the error of segmentation. Then, we extract the characteristics of each cell using the Zernike and HU moment","authors_text":"Khaoula jayech, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara, Nesrine Trimech","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-20T22:02:54Z","title":"Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.5248","kind":"arxiv","version":1},"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":"sha256:58d61cff6e16b9218036e565e6bbbf463771a8a515ba32cc2b11617e6a6ab92e","target":"record","created_at":"2026-05-18T02:51:24Z","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":"ec3383ef7180ab66d586992ad2268d1c47b352b5656706d43fab63da582ab5d5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-20T22:02:54Z","title_canon_sha256":"1467e89e5648be0a1d34c2ab3bb6a092a0b443256b659ec7a16a3ee9b1bc994b"},"schema_version":"1.0","source":{"id":"1405.5248","kind":"arxiv","version":1}},"canonical_sha256":"da6a6734a5d0647f09022b9fe429e843cc15e10e5d472d61416d631ede1426d1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"da6a6734a5d0647f09022b9fe429e843cc15e10e5d472d61416d631ede1426d1","first_computed_at":"2026-05-18T02:51:24.701660Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:51:24.701660Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"as6Vpuqp+OgV8IaOEtEXh5EyC9GVlAbJR+BXNb8C4nayRiz6WeVVbrUX/HDo00NlUc7/lhyr0GTPnz+NzSV7DA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:51:24.702276Z","signed_message":"canonical_sha256_bytes"},"source_id":"1405.5248","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:58d61cff6e16b9218036e565e6bbbf463771a8a515ba32cc2b11617e6a6ab92e","sha256:045f59cb9f8dc1b656fb8a791d0f2d51b6c829d64f61e08942794e14c688f2b8"],"state_sha256":"cacca5e1b87bbb540ae2ab2b3975733bf1ff8faa6c95cc9e600c4632bff7c789"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ggGlS9q3R4lXBlJ6EaTg0pCMXWckope/Zv26vLqnlZ4JA77yckHf3EHGv9h4EFAxrb/+f/0p2yHUtYVXe2XnDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T14:14:36.965148Z","bundle_sha256":"438432b3393f3241da5a2c26cdca0d75843076ee5a719645a12ad767c67ac646"}}