{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:5SJ47PSKRQGRCK7JDQIVEAZ4GB","short_pith_number":"pith:5SJ47PSK","canonical_record":{"source":{"id":"1911.09026","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-19T09:55:27Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"37ff82affb4a42a12d06ca9bed8e6c959155b44fd96963bf6c4f58bfb86a5d7b","abstract_canon_sha256":"0448c4d6e61566f6269089c0e795571e5f498adb33052db753863f9215d59a44"},"schema_version":"1.0"},"canonical_sha256":"ec93cfbe4a8c0d112be91c1152033c306e10e23dd32e2c072e3bba8d54b625a9","source":{"kind":"arxiv","id":"1911.09026","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.09026","created_at":"2026-07-05T01:13:55Z"},{"alias_kind":"arxiv_version","alias_value":"1911.09026v1","created_at":"2026-07-05T01:13:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.09026","created_at":"2026-07-05T01:13:55Z"},{"alias_kind":"pith_short_12","alias_value":"5SJ47PSKRQGR","created_at":"2026-07-05T01:13:55Z"},{"alias_kind":"pith_short_16","alias_value":"5SJ47PSKRQGRCK7J","created_at":"2026-07-05T01:13:55Z"},{"alias_kind":"pith_short_8","alias_value":"5SJ47PSK","created_at":"2026-07-05T01:13:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:5SJ47PSKRQGRCK7JDQIVEAZ4GB","target":"record","payload":{"canonical_record":{"source":{"id":"1911.09026","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-19T09:55:27Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"37ff82affb4a42a12d06ca9bed8e6c959155b44fd96963bf6c4f58bfb86a5d7b","abstract_canon_sha256":"0448c4d6e61566f6269089c0e795571e5f498adb33052db753863f9215d59a44"},"schema_version":"1.0"},"canonical_sha256":"ec93cfbe4a8c0d112be91c1152033c306e10e23dd32e2c072e3bba8d54b625a9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:13:55.717296Z","signature_b64":"iCQV8jqHmxSAkIl4RsM5G99ij7AoRt9ieTHp0xVJIIXZKsBcs63uBW3ojGArkLAp5h9gS9JKJhqqHAlAFP+xBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec93cfbe4a8c0d112be91c1152033c306e10e23dd32e2c072e3bba8d54b625a9","last_reissued_at":"2026-07-05T01:13:55.716842Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:13:55.716842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1911.09026","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":"2026-07-05T01:13:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3HKlmfWtHhOz8lJcSsgSMCAyR0n4M6zgBizDeCIf/+h+ad2Hfl05kLdVc0kMW87zanF5s+xOCjE0EfUeTXZZCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T15:38:17.782825Z"},"content_sha256":"c0e720776fb57a4c3e6140af7a3d628f760d28aa1fad47ed6cf65e6c4cd69c69","schema_version":"1.0","event_id":"sha256:c0e720776fb57a4c3e6140af7a3d628f760d28aa1fad47ed6cf65e6c4cd69c69"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:5SJ47PSKRQGRCK7JDQIVEAZ4GB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Weak Supervision for Generating Pixel-Level Annotations in Scene Text Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Franco Scarselli, Monica Bianchini, Paolo Andreini, Simone Bonechi","submitted_at":"2019-11-19T09:55:27Z","abstract_excerpt":"Providing pixel-level supervisions for scene text segmentation is inherently difficult and costly, so that only few small datasets are available for this task. To face the scarcity of training data, previous approaches based on Convolutional Neural Networks (CNNs) rely on the use of a synthetic dataset for pre-training. However, synthetic data cannot reproduce the complexity and variability of natural images. In this work, we propose to use a weakly supervised learning approach to reduce the domain-shift between synthetic and real data. Leveraging the bounding-box supervision of the COCO-Text "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.09026","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1911.09026/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:13:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S2rmh8G7yA8/CXYV7qrVNbf/13wEscCPAZreIDzlmEpKlaaE/KWKosCUFox329wakqeVTx/253Obw2W2dcK9Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T15:38:17.783333Z"},"content_sha256":"db9040141f0edee3e1762b54324fef973e53db6eb875e7c2d3a2da124f58a906","schema_version":"1.0","event_id":"sha256:db9040141f0edee3e1762b54324fef973e53db6eb875e7c2d3a2da124f58a906"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5SJ47PSKRQGRCK7JDQIVEAZ4GB/bundle.json","state_url":"https://pith.science/pith/5SJ47PSKRQGRCK7JDQIVEAZ4GB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5SJ47PSKRQGRCK7JDQIVEAZ4GB/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":"2026-08-03T15:38:17Z","links":{"resolver":"https://pith.science/pith/5SJ47PSKRQGRCK7JDQIVEAZ4GB","bundle":"https://pith.science/pith/5SJ47PSKRQGRCK7JDQIVEAZ4GB/bundle.json","state":"https://pith.science/pith/5SJ47PSKRQGRCK7JDQIVEAZ4GB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5SJ47PSKRQGRCK7JDQIVEAZ4GB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:5SJ47PSKRQGRCK7JDQIVEAZ4GB","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":"0448c4d6e61566f6269089c0e795571e5f498adb33052db753863f9215d59a44","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-19T09:55:27Z","title_canon_sha256":"37ff82affb4a42a12d06ca9bed8e6c959155b44fd96963bf6c4f58bfb86a5d7b"},"schema_version":"1.0","source":{"id":"1911.09026","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.09026","created_at":"2026-07-05T01:13:55Z"},{"alias_kind":"arxiv_version","alias_value":"1911.09026v1","created_at":"2026-07-05T01:13:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.09026","created_at":"2026-07-05T01:13:55Z"},{"alias_kind":"pith_short_12","alias_value":"5SJ47PSKRQGR","created_at":"2026-07-05T01:13:55Z"},{"alias_kind":"pith_short_16","alias_value":"5SJ47PSKRQGRCK7J","created_at":"2026-07-05T01:13:55Z"},{"alias_kind":"pith_short_8","alias_value":"5SJ47PSK","created_at":"2026-07-05T01:13:55Z"}],"graph_snapshots":[{"event_id":"sha256:db9040141f0edee3e1762b54324fef973e53db6eb875e7c2d3a2da124f58a906","target":"graph","created_at":"2026-07-05T01:13:55Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1911.09026/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Providing pixel-level supervisions for scene text segmentation is inherently difficult and costly, so that only few small datasets are available for this task. To face the scarcity of training data, previous approaches based on Convolutional Neural Networks (CNNs) rely on the use of a synthetic dataset for pre-training. However, synthetic data cannot reproduce the complexity and variability of natural images. In this work, we propose to use a weakly supervised learning approach to reduce the domain-shift between synthetic and real data. Leveraging the bounding-box supervision of the COCO-Text ","authors_text":"Franco Scarselli, Monica Bianchini, Paolo Andreini, Simone Bonechi","cross_cats":["cs.LG","eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-19T09:55:27Z","title":"Weak Supervision for Generating Pixel-Level Annotations in Scene Text Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.09026","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:c0e720776fb57a4c3e6140af7a3d628f760d28aa1fad47ed6cf65e6c4cd69c69","target":"record","created_at":"2026-07-05T01:13:55Z","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":"0448c4d6e61566f6269089c0e795571e5f498adb33052db753863f9215d59a44","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-19T09:55:27Z","title_canon_sha256":"37ff82affb4a42a12d06ca9bed8e6c959155b44fd96963bf6c4f58bfb86a5d7b"},"schema_version":"1.0","source":{"id":"1911.09026","kind":"arxiv","version":1}},"canonical_sha256":"ec93cfbe4a8c0d112be91c1152033c306e10e23dd32e2c072e3bba8d54b625a9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ec93cfbe4a8c0d112be91c1152033c306e10e23dd32e2c072e3bba8d54b625a9","first_computed_at":"2026-07-05T01:13:55.716842Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:13:55.716842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iCQV8jqHmxSAkIl4RsM5G99ij7AoRt9ieTHp0xVJIIXZKsBcs63uBW3ojGArkLAp5h9gS9JKJhqqHAlAFP+xBg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:13:55.717296Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.09026","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c0e720776fb57a4c3e6140af7a3d628f760d28aa1fad47ed6cf65e6c4cd69c69","sha256:db9040141f0edee3e1762b54324fef973e53db6eb875e7c2d3a2da124f58a906"],"state_sha256":"923c542c3ce7a6dae8bfdf025861e599a85464568a88dce5d98cd68a81f2512d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B6/uBqJ+SNEOY/rrJ+y/oYmb3ZQVHN3LTQrn7Yp37Qqo1nKk27rL8bLNjo2CRoHzFixcIormLUeRWB8zrBRSDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T15:38:17.817682Z","bundle_sha256":"17bdc03185ff2cc75da1dba1156e1654c85903ce6b4f035c7601b3777130ba2d"}}