{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:3OE53XQISR4QFWURIAUKMHIFGO","short_pith_number":"pith:3OE53XQI","canonical_record":{"source":{"id":"2009.02491","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-05T08:14:35Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4769824f2c05bfe4496cd8f866984d304c1ef391fc7fa5da1a2af511f9a90621","abstract_canon_sha256":"65dc6efed18fbafaaa33eca17dfbca85ffe16b76e7fd517c14efeb23bfeffb44"},"schema_version":"1.0"},"canonical_sha256":"db89ddde08947902da914028a61d0533a21cf5386bec801623270f370f9c0ae4","source":{"kind":"arxiv","id":"2009.02491","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.02491","created_at":"2026-07-05T01:33:09Z"},{"alias_kind":"arxiv_version","alias_value":"2009.02491v1","created_at":"2026-07-05T01:33:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.02491","created_at":"2026-07-05T01:33:09Z"},{"alias_kind":"pith_short_12","alias_value":"3OE53XQISR4Q","created_at":"2026-07-05T01:33:09Z"},{"alias_kind":"pith_short_16","alias_value":"3OE53XQISR4QFWUR","created_at":"2026-07-05T01:33:09Z"},{"alias_kind":"pith_short_8","alias_value":"3OE53XQI","created_at":"2026-07-05T01:33:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:3OE53XQISR4QFWURIAUKMHIFGO","target":"record","payload":{"canonical_record":{"source":{"id":"2009.02491","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-05T08:14:35Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4769824f2c05bfe4496cd8f866984d304c1ef391fc7fa5da1a2af511f9a90621","abstract_canon_sha256":"65dc6efed18fbafaaa33eca17dfbca85ffe16b76e7fd517c14efeb23bfeffb44"},"schema_version":"1.0"},"canonical_sha256":"db89ddde08947902da914028a61d0533a21cf5386bec801623270f370f9c0ae4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:33:09.185493Z","signature_b64":"vSnEPzkNWri0Nt0c0wowyrFybxFyrtVXDv/q4QHWUck5Wdg3uNASPqZNO51xEWB/CMtLYtNKWyz2KT33fAtUAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db89ddde08947902da914028a61d0533a21cf5386bec801623270f370f9c0ae4","last_reissued_at":"2026-07-05T01:33:09.185100Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:33:09.185100Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2009.02491","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":"2026-07-05T01:33:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nQRsGIEJ4ArL83I8/jiVbskFO5UiQitrDSVddgsw37hAyEaf+RjmfaMkc3l+uMrwpboxBWHFn3qWU0BWU9LyDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T14:58:29.713951Z"},"content_sha256":"d63cc4a1869b563339ceb622a7dd5cae9a76d1d622ef6ed8e9875e3f70f51393","schema_version":"1.0","event_id":"sha256:d63cc4a1869b563339ceb622a7dd5cae9a76d1d622ef6ed8e9875e3f70f51393"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:3OE53XQISR4QFWURIAUKMHIFGO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reverse-engineering Bar Charts Using Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chao Liu, Fangfang Zhou, Wenjiang Chen, Yaqi Xu, Yi Chen, Yijing Tan, Ying Zhao, Yong Zhao","submitted_at":"2020-09-05T08:14:35Z","abstract_excerpt":"Reverse-engineering bar charts extracts textual and numeric information from the visual representations of bar charts to support application scenarios that require the underlying information. In this paper, we propose a neural network-based method for reverse-engineering bar charts. We adopt a neural network-based object detection model to simultaneously localize and classify textual information. This approach improves the efficiency of textual information extraction. We design an encoder-decoder framework that integrates convolutional and recurrent neural networks to extract numeric informati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.02491","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/2009.02491/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":"2026-07-05T01:33:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"goApFNcVwG2ANsJ6qDs/+8yY7CLRBvNpgvXrCcZSz3aOWXtv2VVsb0xXJ4sAAopq/nvMVOiqWUbcaet+rq2bBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T14:58:29.714348Z"},"content_sha256":"380958613320279a60e339913058a6485dc6eba1e0422e63d2985d613ee457ab","schema_version":"1.0","event_id":"sha256:380958613320279a60e339913058a6485dc6eba1e0422e63d2985d613ee457ab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3OE53XQISR4QFWURIAUKMHIFGO/bundle.json","state_url":"https://pith.science/pith/3OE53XQISR4QFWURIAUKMHIFGO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3OE53XQISR4QFWURIAUKMHIFGO/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":"2026-07-24T14:58:29Z","links":{"resolver":"https://pith.science/pith/3OE53XQISR4QFWURIAUKMHIFGO","bundle":"https://pith.science/pith/3OE53XQISR4QFWURIAUKMHIFGO/bundle.json","state":"https://pith.science/pith/3OE53XQISR4QFWURIAUKMHIFGO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3OE53XQISR4QFWURIAUKMHIFGO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:3OE53XQISR4QFWURIAUKMHIFGO","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":"65dc6efed18fbafaaa33eca17dfbca85ffe16b76e7fd517c14efeb23bfeffb44","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-05T08:14:35Z","title_canon_sha256":"4769824f2c05bfe4496cd8f866984d304c1ef391fc7fa5da1a2af511f9a90621"},"schema_version":"1.0","source":{"id":"2009.02491","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.02491","created_at":"2026-07-05T01:33:09Z"},{"alias_kind":"arxiv_version","alias_value":"2009.02491v1","created_at":"2026-07-05T01:33:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.02491","created_at":"2026-07-05T01:33:09Z"},{"alias_kind":"pith_short_12","alias_value":"3OE53XQISR4Q","created_at":"2026-07-05T01:33:09Z"},{"alias_kind":"pith_short_16","alias_value":"3OE53XQISR4QFWUR","created_at":"2026-07-05T01:33:09Z"},{"alias_kind":"pith_short_8","alias_value":"3OE53XQI","created_at":"2026-07-05T01:33:09Z"}],"graph_snapshots":[{"event_id":"sha256:380958613320279a60e339913058a6485dc6eba1e0422e63d2985d613ee457ab","target":"graph","created_at":"2026-07-05T01:33:09Z","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/2009.02491/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reverse-engineering bar charts extracts textual and numeric information from the visual representations of bar charts to support application scenarios that require the underlying information. In this paper, we propose a neural network-based method for reverse-engineering bar charts. We adopt a neural network-based object detection model to simultaneously localize and classify textual information. This approach improves the efficiency of textual information extraction. We design an encoder-decoder framework that integrates convolutional and recurrent neural networks to extract numeric informati","authors_text":"Chao Liu, Fangfang Zhou, Wenjiang Chen, Yaqi Xu, Yi Chen, Yijing Tan, Ying Zhao, Yong Zhao","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-05T08:14:35Z","title":"Reverse-engineering Bar Charts Using Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.02491","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:d63cc4a1869b563339ceb622a7dd5cae9a76d1d622ef6ed8e9875e3f70f51393","target":"record","created_at":"2026-07-05T01:33:09Z","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":"65dc6efed18fbafaaa33eca17dfbca85ffe16b76e7fd517c14efeb23bfeffb44","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-05T08:14:35Z","title_canon_sha256":"4769824f2c05bfe4496cd8f866984d304c1ef391fc7fa5da1a2af511f9a90621"},"schema_version":"1.0","source":{"id":"2009.02491","kind":"arxiv","version":1}},"canonical_sha256":"db89ddde08947902da914028a61d0533a21cf5386bec801623270f370f9c0ae4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db89ddde08947902da914028a61d0533a21cf5386bec801623270f370f9c0ae4","first_computed_at":"2026-07-05T01:33:09.185100Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:33:09.185100Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vSnEPzkNWri0Nt0c0wowyrFybxFyrtVXDv/q4QHWUck5Wdg3uNASPqZNO51xEWB/CMtLYtNKWyz2KT33fAtUAg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:33:09.185493Z","signed_message":"canonical_sha256_bytes"},"source_id":"2009.02491","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d63cc4a1869b563339ceb622a7dd5cae9a76d1d622ef6ed8e9875e3f70f51393","sha256:380958613320279a60e339913058a6485dc6eba1e0422e63d2985d613ee457ab"],"state_sha256":"eab95cebca8b5a3c58cc01dda20c3519baf839f3695a8d13eb90485e7e6da761"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U/1KcSL8DSKQd+RiX5LBB/lcK2jtCatalP9y5/rIjDhnqx8usG+t2PXnZ7SfBZ5nuXHMgOUGpGg1Brcv+4URAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-24T14:58:29.716523Z","bundle_sha256":"cbcfdaee6f53689cc23edda2b1c5725f27623875df728e3e2ce563f5cb7b4e05"}}