mlsanityData to 7 Oct 2026

OCR Improves Machine Translation for Low-Resource Languages

arXiv 2202.13274 ↗First posted 27 Feb 2022cs.CLcited by 5

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Abstract (arXiv)

We aim to investigate the performance of current OCR systems on low resource languages and low resource scripts. We introduce and make publicly available a novel benchmark, OCR4MT, consisting of real and synthetic data, enriched with noise, for 60 low-resource languages in low resource scripts. We evaluate state-of-the-art OCR systems on our benchmark and analyse most common errors. We show that OCR monolingual data is a valuable resource that can increase performance of Machine Translation models, when used in backtranslation. We then perform an ablation study to investigate how OCR errors impact Machine Translation performance and determine what is the minimum level of OCR quality needed for the monolingual data to be useful for Machine Translation.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 27 Feb 2022.

Builds on

14 of 29 references.

References · 14

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  2. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  3. 2020Beyond English-Centric Multilingual Machine Translationcited by 559
  4. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 556
  5. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 544
  6. 2020Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translationcited by 277
  7. 2019WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipediacited by 221
  8. 2019Soft Contextual Data Augmentation for Neural Machine Translationcited by 49
  9. 2020OCR Post Correction for Endangered Language Textscited by 31
  10. 2019A Scalable Handwritten Text Recognition Systemcited by 25
  11. 2021Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoderscited by 24
  12. 2018Calamari - A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognitioncited by 15
2 more
  1. 2017Sequence-to-Label Script Identification for Multilingual OCRcited by 12
  2. 2021Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documentscited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languagescited by 34
  2. 2023Noisy Parallel Data Alignmentcited by 1
  3. 2024Optical Text Recognition in Nepali and Bengali: A Transformer-based Approachcited by 1
  4. 2026Typhoon OCR: Open Vision-Language Model For Thai Document Extractioncited by 1
  5. 2025PsOCR: Benchmarking Large Multimodal Models for Optical Character Recognition in Low-resource Pashto Languagecited by 0