{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ZKII5KVQQQ2VUPA3GDNJFFQ3PB","short_pith_number":"pith:ZKII5KVQ","canonical_record":{"source":{"id":"1708.03273","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T15:50:30Z","cross_cats_sorted":[],"title_canon_sha256":"8d30fd4ab2fd8799260f3d00f81d59b8e0bb259994e8db86900d2243c6578489","abstract_canon_sha256":"c858407e02af6ba4ecc61d7bc9b696fadcaf5db4a30a4fbcacd4fe0e05ac18d5"},"schema_version":"1.0"},"canonical_sha256":"ca908eaab084355a3c1b30da92961b785e71acf83c3424b499328495cca3ff40","source":{"kind":"arxiv","id":"1708.03273","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.03273","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"arxiv_version","alias_value":"1708.03273v1","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.03273","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"pith_short_12","alias_value":"ZKII5KVQQQ2V","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZKII5KVQQQ2VUPA3","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZKII5KVQ","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ZKII5KVQQQ2VUPA3GDNJFFQ3PB","target":"record","payload":{"canonical_record":{"source":{"id":"1708.03273","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T15:50:30Z","cross_cats_sorted":[],"title_canon_sha256":"8d30fd4ab2fd8799260f3d00f81d59b8e0bb259994e8db86900d2243c6578489","abstract_canon_sha256":"c858407e02af6ba4ecc61d7bc9b696fadcaf5db4a30a4fbcacd4fe0e05ac18d5"},"schema_version":"1.0"},"canonical_sha256":"ca908eaab084355a3c1b30da92961b785e71acf83c3424b499328495cca3ff40","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:14.262970Z","signature_b64":"KTND7tuAGZh44wcYcLbzymfnlMjzRq3/BmxNNFiVi3AP0SijyNbEV2iUWkRB4vAW7mmyrFm4lSbOQIKflVHCCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca908eaab084355a3c1b30da92961b785e71acf83c3424b499328495cca3ff40","last_reissued_at":"2026-05-18T00:38:14.262246Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:14.262246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.03273","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":"BGXoFg8/08IFQrS5f0FTffCyQlyLGcsYYb1Px0RALjUZo1vEn/ie5USPaFEo19qVNRdY+7+TjMjQ5yQM/DzTCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T04:56:30.121691Z"},"content_sha256":"2662f59c042215b0de54013e2f21c850192415698a69aa0674f7b42f1644705a","schema_version":"1.0","event_id":"sha256:2662f59c042215b0de54013e2f21c850192415698a69aa0674f7b42f1644705a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ZKII5KVQQQ2VUPA3GDNJFFQ3PB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Analysis of Convolutional Neural Networks for Document Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chris Tensmeyer, Tony Martinez","submitted_at":"2017-08-10T15:50:30Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) are state-of-the-art models for document image classification tasks. However, many of these approaches rely on parameters and architectures designed for classifying natural images, which differ from document images. We question whether this is appropriate and conduct a large empirical study to find what aspects of CNNs most affect performance on document images. Among other results, we exceed the state-of-the-art on the RVL-CDIP dataset by using shear transform data augmentation and an architecture designed for a larger input image. Additionally, we analyze"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.03273","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":"tRvtdnzS167jxqDNOxjxpGEE4ogqKldOD5fC8497t9sJQd7kp1HZixPg5udbCrKzANv5jtyPo6/5up0T/xk+Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T04:56:30.122041Z"},"content_sha256":"4e746775e05436d5ebe2d63bf543cb9eb8dcb5d482de100cb4a8ee8756db9824","schema_version":"1.0","event_id":"sha256:4e746775e05436d5ebe2d63bf543cb9eb8dcb5d482de100cb4a8ee8756db9824"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZKII5KVQQQ2VUPA3GDNJFFQ3PB/bundle.json","state_url":"https://pith.science/pith/ZKII5KVQQQ2VUPA3GDNJFFQ3PB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZKII5KVQQQ2VUPA3GDNJFFQ3PB/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":"2026-06-21T04:56:30Z","links":{"resolver":"https://pith.science/pith/ZKII5KVQQQ2VUPA3GDNJFFQ3PB","bundle":"https://pith.science/pith/ZKII5KVQQQ2VUPA3GDNJFFQ3PB/bundle.json","state":"https://pith.science/pith/ZKII5KVQQQ2VUPA3GDNJFFQ3PB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZKII5KVQQQ2VUPA3GDNJFFQ3PB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ZKII5KVQQQ2VUPA3GDNJFFQ3PB","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":"c858407e02af6ba4ecc61d7bc9b696fadcaf5db4a30a4fbcacd4fe0e05ac18d5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T15:50:30Z","title_canon_sha256":"8d30fd4ab2fd8799260f3d00f81d59b8e0bb259994e8db86900d2243c6578489"},"schema_version":"1.0","source":{"id":"1708.03273","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.03273","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"arxiv_version","alias_value":"1708.03273v1","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.03273","created_at":"2026-05-18T00:38:14Z"},{"alias_kind":"pith_short_12","alias_value":"ZKII5KVQQQ2V","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZKII5KVQQQ2VUPA3","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZKII5KVQ","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:4e746775e05436d5ebe2d63bf543cb9eb8dcb5d482de100cb4a8ee8756db9824","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":"Convolutional Neural Networks (CNNs) are state-of-the-art models for document image classification tasks. However, many of these approaches rely on parameters and architectures designed for classifying natural images, which differ from document images. We question whether this is appropriate and conduct a large empirical study to find what aspects of CNNs most affect performance on document images. Among other results, we exceed the state-of-the-art on the RVL-CDIP dataset by using shear transform data augmentation and an architecture designed for a larger input image. Additionally, we analyze","authors_text":"Chris Tensmeyer, Tony Martinez","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T15:50:30Z","title":"Analysis of Convolutional Neural Networks for Document Image Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.03273","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":"sha256:2662f59c042215b0de54013e2f21c850192415698a69aa0674f7b42f1644705a","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":"c858407e02af6ba4ecc61d7bc9b696fadcaf5db4a30a4fbcacd4fe0e05ac18d5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T15:50:30Z","title_canon_sha256":"8d30fd4ab2fd8799260f3d00f81d59b8e0bb259994e8db86900d2243c6578489"},"schema_version":"1.0","source":{"id":"1708.03273","kind":"arxiv","version":1}},"canonical_sha256":"ca908eaab084355a3c1b30da92961b785e71acf83c3424b499328495cca3ff40","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ca908eaab084355a3c1b30da92961b785e71acf83c3424b499328495cca3ff40","first_computed_at":"2026-05-18T00:38:14.262246Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:14.262246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KTND7tuAGZh44wcYcLbzymfnlMjzRq3/BmxNNFiVi3AP0SijyNbEV2iUWkRB4vAW7mmyrFm4lSbOQIKflVHCCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:14.262970Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.03273","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2662f59c042215b0de54013e2f21c850192415698a69aa0674f7b42f1644705a","sha256:4e746775e05436d5ebe2d63bf543cb9eb8dcb5d482de100cb4a8ee8756db9824"],"state_sha256":"56c051edc2b3c6bc3ef2623ab7cd71eb84d55386182da8055c27958b4ad74bf6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uWF8uhHeeKRKEzf/zueh0Zu05ezDIO/rXEDOVOKqMw/1SYRMdncEb/FyDT0vvtpTdNYUg2+y0+ljIbPrQCB4AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-21T04:56:30.124082Z","bundle_sha256":"bab11eb51b9c8d6af178eb50dbdd80d2bc0a74e4267ee63cdd6d13f17fdb0b84"}}