{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:C5TNWBBOOE73UOWZRKZ3IZEAIE","short_pith_number":"pith:C5TNWBBO","canonical_record":{"source":{"id":"1708.03276","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T16:00:35Z","cross_cats_sorted":[],"title_canon_sha256":"3e235dfea068b1fb04598c4e8fb2417c74743f04168556e126a794e12ac2b2f4","abstract_canon_sha256":"77938fadc3f8ded6e94a4e357c7e2e546b5fb22a2a728f08e67d04d58e32b3ec"},"schema_version":"1.0"},"canonical_sha256":"1766db042e713fba3ad98ab3b46480411a398d365c52a0d09deaf956786e9677","source":{"kind":"arxiv","id":"1708.03276","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.03276","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"arxiv_version","alias_value":"1708.03276v1","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.03276","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"pith_short_12","alias_value":"C5TNWBBOOE73","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"C5TNWBBOOE73UOWZ","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"C5TNWBBO","created_at":"2026-05-18T12:31:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:C5TNWBBOOE73UOWZRKZ3IZEAIE","target":"record","payload":{"canonical_record":{"source":{"id":"1708.03276","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T16:00:35Z","cross_cats_sorted":[],"title_canon_sha256":"3e235dfea068b1fb04598c4e8fb2417c74743f04168556e126a794e12ac2b2f4","abstract_canon_sha256":"77938fadc3f8ded6e94a4e357c7e2e546b5fb22a2a728f08e67d04d58e32b3ec"},"schema_version":"1.0"},"canonical_sha256":"1766db042e713fba3ad98ab3b46480411a398d365c52a0d09deaf956786e9677","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:14.243846Z","signature_b64":"mfvGEWRnaWe5bDfPu7uf9Rg9Rf8x3nmUn8YkOaqE4qFNaSGyWORpXS656i6X/hW9EfSBI6KW9cYPewn7YVYjDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1766db042e713fba3ad98ab3b46480411a398d365c52a0d09deaf956786e9677","last_reissued_at":"2026-05-18T00:38:14.243189Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:14.243189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.03276","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":"2026-05-18T00:38:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ef6k7AWyNRKTdiySnPmoKR7cr93YapNPD0VhD4b+P53r7csnNpsMRPX1ankBtfqEQkuCmMpOFvgdgqbh4IyhDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T23:28:20.228398Z"},"content_sha256":"34f2ade5586d59a27cb6f9fec7dd147364f6354b7742ab52a0ca3b58d977bef5","schema_version":"1.0","event_id":"sha256:34f2ade5586d59a27cb6f9fec7dd147364f6354b7742ab52a0ca3b58d977bef5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:C5TNWBBOOE73UOWZRKZ3IZEAIE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Document Image Binarization with Fully Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chris Tensmeyer, Tony Martinez","submitted_at":"2017-08-10T16:00:35Z","abstract_excerpt":"Binarization of degraded historical manuscript images is an important pre-processing step for many document processing tasks. We formulate binarization as a pixel classification learning task and apply a novel Fully Convolutional Network (FCN) architecture that operates at multiple image scales, including full resolution. The FCN is trained to optimize a continuous version of the Pseudo F-measure metric and an ensemble of FCNs outperform the competition winners on 4 of 7 DIBCO competitions. This same binarization technique can also be applied to different domains such as Palm Leaf Manuscripts "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.03276","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":"2026-05-18T00:38:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ysxS0LWh13gTKOQ11yZTWjS+TWpORvhO5h/3mPc8lAgwumIXzLh7BDrzhOZxOG+pjeYGNON7sUyUS5KWQvcDDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T23:28:20.228751Z"},"content_sha256":"9e8e730206188091a6f8599e1fbae272296ffd2f6bcd74492b15e9126cec4430","schema_version":"1.0","event_id":"sha256:9e8e730206188091a6f8599e1fbae272296ffd2f6bcd74492b15e9126cec4430"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C5TNWBBOOE73UOWZRKZ3IZEAIE/bundle.json","state_url":"https://pith.science/pith/C5TNWBBOOE73UOWZRKZ3IZEAIE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C5TNWBBOOE73UOWZRKZ3IZEAIE/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":"2026-06-20T23:28:20Z","links":{"resolver":"https://pith.science/pith/C5TNWBBOOE73UOWZRKZ3IZEAIE","bundle":"https://pith.science/pith/C5TNWBBOOE73UOWZRKZ3IZEAIE/bundle.json","state":"https://pith.science/pith/C5TNWBBOOE73UOWZRKZ3IZEAIE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C5TNWBBOOE73UOWZRKZ3IZEAIE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:C5TNWBBOOE73UOWZRKZ3IZEAIE","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":"77938fadc3f8ded6e94a4e357c7e2e546b5fb22a2a728f08e67d04d58e32b3ec","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T16:00:35Z","title_canon_sha256":"3e235dfea068b1fb04598c4e8fb2417c74743f04168556e126a794e12ac2b2f4"},"schema_version":"1.0","source":{"id":"1708.03276","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.03276","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"arxiv_version","alias_value":"1708.03276v1","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.03276","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"pith_short_12","alias_value":"C5TNWBBOOE73","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"C5TNWBBOOE73UOWZ","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"C5TNWBBO","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:9e8e730206188091a6f8599e1fbae272296ffd2f6bcd74492b15e9126cec4430","target":"graph","created_at":"2026-05-18T00:38:14Z","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":"Binarization of degraded historical manuscript images is an important pre-processing step for many document processing tasks. We formulate binarization as a pixel classification learning task and apply a novel Fully Convolutional Network (FCN) architecture that operates at multiple image scales, including full resolution. The FCN is trained to optimize a continuous version of the Pseudo F-measure metric and an ensemble of FCNs outperform the competition winners on 4 of 7 DIBCO competitions. This same binarization technique can also be applied to different domains such as Palm Leaf Manuscripts ","authors_text":"Chris Tensmeyer, Tony Martinez","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T16:00:35Z","title":"Document Image Binarization with Fully Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.03276","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":"sha256:34f2ade5586d59a27cb6f9fec7dd147364f6354b7742ab52a0ca3b58d977bef5","target":"record","created_at":"2026-05-18T00:38:14Z","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":"77938fadc3f8ded6e94a4e357c7e2e546b5fb22a2a728f08e67d04d58e32b3ec","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T16:00:35Z","title_canon_sha256":"3e235dfea068b1fb04598c4e8fb2417c74743f04168556e126a794e12ac2b2f4"},"schema_version":"1.0","source":{"id":"1708.03276","kind":"arxiv","version":1}},"canonical_sha256":"1766db042e713fba3ad98ab3b46480411a398d365c52a0d09deaf956786e9677","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1766db042e713fba3ad98ab3b46480411a398d365c52a0d09deaf956786e9677","first_computed_at":"2026-05-18T00:38:14.243189Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:14.243189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mfvGEWRnaWe5bDfPu7uf9Rg9Rf8x3nmUn8YkOaqE4qFNaSGyWORpXS656i6X/hW9EfSBI6KW9cYPewn7YVYjDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:14.243846Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.03276","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:34f2ade5586d59a27cb6f9fec7dd147364f6354b7742ab52a0ca3b58d977bef5","sha256:9e8e730206188091a6f8599e1fbae272296ffd2f6bcd74492b15e9126cec4430"],"state_sha256":"887831453adebc4f7bcfaeca7ee4776fefee6d123ecacdae3d97a7e346a49f38"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XgBvPp6X3hu+h+fDIkdWiJF59FlJ/JCyLeqvBU5M8tJXrTJg7c/zkM14/ostKrxiTGoH95e1fLBW8MKyHok7Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T23:28:20.230703Z","bundle_sha256":"928ecd9a0ab69723fbfcc4f99aa32373ab54bb1c720e9b961fa4192f5735ab62"}}