{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:5W4EX2JB7DIUOWJXEVK4COMBZ5","short_pith_number":"pith:5W4EX2JB","canonical_record":{"source":{"id":"2305.05166","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-09T04:25:52Z","cross_cats_sorted":[],"title_canon_sha256":"063d2674953b877c1a50f42b490b5d417475f98089423d5ff66efec087a575b9","abstract_canon_sha256":"5596600c03bf2fab13a84f3e739304ce4f9e43441ce3524bcefa13b81118dcda"},"schema_version":"1.0"},"canonical_sha256":"edb84be921f8d14759372555c13981cf47823194a706534665a9ed20e8a1bf86","source":{"kind":"arxiv","id":"2305.05166","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.05166","created_at":"2026-07-05T06:08:55Z"},{"alias_kind":"arxiv_version","alias_value":"2305.05166v2","created_at":"2026-07-05T06:08:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.05166","created_at":"2026-07-05T06:08:55Z"},{"alias_kind":"pith_short_12","alias_value":"5W4EX2JB7DIU","created_at":"2026-07-05T06:08:55Z"},{"alias_kind":"pith_short_16","alias_value":"5W4EX2JB7DIUOWJX","created_at":"2026-07-05T06:08:55Z"},{"alias_kind":"pith_short_8","alias_value":"5W4EX2JB","created_at":"2026-07-05T06:08:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:5W4EX2JB7DIUOWJXEVK4COMBZ5","target":"record","payload":{"canonical_record":{"source":{"id":"2305.05166","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-09T04:25:52Z","cross_cats_sorted":[],"title_canon_sha256":"063d2674953b877c1a50f42b490b5d417475f98089423d5ff66efec087a575b9","abstract_canon_sha256":"5596600c03bf2fab13a84f3e739304ce4f9e43441ce3524bcefa13b81118dcda"},"schema_version":"1.0"},"canonical_sha256":"edb84be921f8d14759372555c13981cf47823194a706534665a9ed20e8a1bf86","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:08:55.991811Z","signature_b64":"zwYhJ/bhviVPEpCBJANpB9tAIpt4cFPuySPzNMpjCy9gkZasvTQK29Z7r2xvLwA0n45hZVEpNqYw7tD5E4ivCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"edb84be921f8d14759372555c13981cf47823194a706534665a9ed20e8a1bf86","last_reissued_at":"2026-07-05T06:08:55.991394Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:08:55.991394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.05166","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:08:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"28ta1CcgQ3jz3yJjE0DpBryx8cXY/Z2+th3i0HOmMaziWg6xldb3DQoetsoDv+05an2yUyIvDbEWjtehY7FwCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T06:56:34.597928Z"},"content_sha256":"d67feddf382ab6f6a18f4e24f2a6817e896ce65b0d46ab759865f2d8007f4eac","schema_version":"1.0","event_id":"sha256:d67feddf382ab6f6a18f4e24f2a6817e896ce65b0d46ab759865f2d8007f4eac"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:5W4EX2JB7DIUOWJXEVK4COMBZ5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengqing Zong, Cong Ma, Mei Tu, Yang Zhao, Yaping Zhang, Yu Zhou","submitted_at":"2023-05-09T04:25:52Z","abstract_excerpt":"Text image machine translation (TIMT) aims to translate texts embedded in images from one source language to another target language. Existing methods, both two-stage cascade and one-stage end-to-end architectures, suffer from different issues. The cascade models can benefit from the large-scale optical character recognition (OCR) and MT datasets but the two-stage architecture is redundant. The end-to-end models are efficient but suffer from training data deficiency. To this end, in our paper, we propose an end-to-end TIMT model fully making use of the knowledge from existing OCR and MT datase"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.05166","kind":"arxiv","version":2},"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/2305.05166/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":"2026-07-05T06:08:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KzFQAe2rwm6N9NTVq9Fze3W+uKs9cA0M/YcYQAMyp9PcdQn5+OBNOze6+Gu5fWrIg/eEhKQuWo98mK5bU0PWDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T06:56:34.598301Z"},"content_sha256":"6e1f3354201d3432ce3f9e981d554f0d7a56d419c05cb965d708fd194e86cb60","schema_version":"1.0","event_id":"sha256:6e1f3354201d3432ce3f9e981d554f0d7a56d419c05cb965d708fd194e86cb60"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5W4EX2JB7DIUOWJXEVK4COMBZ5/bundle.json","state_url":"https://pith.science/pith/5W4EX2JB7DIUOWJXEVK4COMBZ5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5W4EX2JB7DIUOWJXEVK4COMBZ5/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":"2026-07-10T06:56:34Z","links":{"resolver":"https://pith.science/pith/5W4EX2JB7DIUOWJXEVK4COMBZ5","bundle":"https://pith.science/pith/5W4EX2JB7DIUOWJXEVK4COMBZ5/bundle.json","state":"https://pith.science/pith/5W4EX2JB7DIUOWJXEVK4COMBZ5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5W4EX2JB7DIUOWJXEVK4COMBZ5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:5W4EX2JB7DIUOWJXEVK4COMBZ5","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":"5596600c03bf2fab13a84f3e739304ce4f9e43441ce3524bcefa13b81118dcda","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-09T04:25:52Z","title_canon_sha256":"063d2674953b877c1a50f42b490b5d417475f98089423d5ff66efec087a575b9"},"schema_version":"1.0","source":{"id":"2305.05166","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.05166","created_at":"2026-07-05T06:08:55Z"},{"alias_kind":"arxiv_version","alias_value":"2305.05166v2","created_at":"2026-07-05T06:08:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.05166","created_at":"2026-07-05T06:08:55Z"},{"alias_kind":"pith_short_12","alias_value":"5W4EX2JB7DIU","created_at":"2026-07-05T06:08:55Z"},{"alias_kind":"pith_short_16","alias_value":"5W4EX2JB7DIUOWJX","created_at":"2026-07-05T06:08:55Z"},{"alias_kind":"pith_short_8","alias_value":"5W4EX2JB","created_at":"2026-07-05T06:08:55Z"}],"graph_snapshots":[{"event_id":"sha256:6e1f3354201d3432ce3f9e981d554f0d7a56d419c05cb965d708fd194e86cb60","target":"graph","created_at":"2026-07-05T06:08:55Z","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/2305.05166/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text image machine translation (TIMT) aims to translate texts embedded in images from one source language to another target language. Existing methods, both two-stage cascade and one-stage end-to-end architectures, suffer from different issues. The cascade models can benefit from the large-scale optical character recognition (OCR) and MT datasets but the two-stage architecture is redundant. The end-to-end models are efficient but suffer from training data deficiency. To this end, in our paper, we propose an end-to-end TIMT model fully making use of the knowledge from existing OCR and MT datase","authors_text":"Chengqing Zong, Cong Ma, Mei Tu, Yang Zhao, Yaping Zhang, Yu Zhou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-09T04:25:52Z","title":"E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.05166","kind":"arxiv","version":2},"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:d67feddf382ab6f6a18f4e24f2a6817e896ce65b0d46ab759865f2d8007f4eac","target":"record","created_at":"2026-07-05T06:08:55Z","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":"5596600c03bf2fab13a84f3e739304ce4f9e43441ce3524bcefa13b81118dcda","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-09T04:25:52Z","title_canon_sha256":"063d2674953b877c1a50f42b490b5d417475f98089423d5ff66efec087a575b9"},"schema_version":"1.0","source":{"id":"2305.05166","kind":"arxiv","version":2}},"canonical_sha256":"edb84be921f8d14759372555c13981cf47823194a706534665a9ed20e8a1bf86","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"edb84be921f8d14759372555c13981cf47823194a706534665a9ed20e8a1bf86","first_computed_at":"2026-07-05T06:08:55.991394Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:08:55.991394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zwYhJ/bhviVPEpCBJANpB9tAIpt4cFPuySPzNMpjCy9gkZasvTQK29Z7r2xvLwA0n45hZVEpNqYw7tD5E4ivCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:08:55.991811Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.05166","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d67feddf382ab6f6a18f4e24f2a6817e896ce65b0d46ab759865f2d8007f4eac","sha256:6e1f3354201d3432ce3f9e981d554f0d7a56d419c05cb965d708fd194e86cb60"],"state_sha256":"bf01af69554ba0d25a34b8f59d5e54163e773d3ee8474b5673365c35eed7a431"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ee5ojaRub4RJ5UFx0QoCp/mQ4wKCACWyvF6tIOQML0oTLiLn0PheGZLZaPK0zRmgIjLdckKArwgNWHiJhH4gBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T06:56:34.600206Z","bundle_sha256":"3e03cd4776e13d2ac4fa6eed37e212ce0878a07f8726a085d80bf56198ab8a20"}}