| Challenge: | Existing methods for OCR correction are mostly supervised methods that correct recognition errors in a single output. |
| Approach: | They propose a sequence-to-sequence model with attention and a decoder with attention averaging to search for consensus among multiple sequences. |
| Outcome: | The proposed methods cut the character and word error rates nearly in half on single inputs and can rival supervised methods. |
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Unsupervised Multi-View Post-OCR Error Correction With Language Models (2021.emnlp-main)
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| Challenge: | Prior work used text generation techniques or redundancy in similar passages for OCR error correction, which is not appropriate in cases of low corpus redundancies or weak document contextual information. |
| Approach: | They propose to use a pretrained language model to reconcile different OCR views in unsupervised way so that their combination contains fewer errors than each individual view. |
| Outcome: | The proposed model can reconcile multiple OCR views so that their combined version contains fewer errors than the best OCR view. |
Lexically Aware Semi-Supervised Learning for OCR Post-Correction (2021.tacl-1)
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| Challenge: | Existing methods for digitizing text in endangered languages rely on manual data curated by the user. |
| Approach: | They propose a semi-supervised learning method that utilizes raw images to improve performance. |
| Outcome: | The proposed method reduces errors by 15%–29% on four endangered languages. |
Neural OCR Post-Hoc Correction of Historical Corpora (2021.tacl-1)
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| Challenge: | Optical character recognition (OCR) is crucial for a deeper access to historical collections. |
| Approach: | They propose a neural approach based on a combination of recurrent (RNN) and deep convolutional network (ConvNet) to correct OCR transcription errors. |
| Outcome: | The proposed model reduces the word error rate of 32.3% by more than 89% on a historical book corpus in German language. |
OCR Post Correction for Endangered Language Texts (2020.emnlp-main)
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| Challenge: | Currently, there is little to no data available to build natural language processing models for endangered languages. |
| Approach: | They propose a benchmark dataset of transcriptions for scanned books in three critically endangered languages and a method to improve OCR in these data-scarce settings. |
| Outcome: | The proposed method reduces the recognition error rate by 34% across the three endangered languages. |
Low-resource Post Processing of Noisy OCR Output for Historical Corpus Digitisation (L18-1)
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| Challenge: | 7.6% of the words in the original OCR text contain an error; fully manual correction would take thousands of hours due to the size of the corpus. |
| Approach: | They propose a post-processing system to efficiently correct OCR errors in a 2.7 million word Faroese corpus. |
| Outcome: | The proposed method reduces the word error rate to 1.3% with around 65 hours of human annotator work. |
AMR Parsing as Sequence-to-Graph Transduction (P19-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is the task of transducing natural language text into AMR, a graphbased formalism used for capturing sentence-level semantics. |
| Approach: | They propose a model that treats AMR parsing as sequence-to-graph transduction by aligner-free, and can be effectively trained with limited amounts of labeled AMR data. |
| Outcome: | The proposed model outperforms all previously reported SMATCH scores on AMR 2.0 (76.3%) and AMR 1.0 (70.2%). |
Attention Optimization for Abstractive Document Summarization (D19-1)
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| Challenge: | Abstractive summarization models require attention to reproduce the most salient information. |
| Approach: | They propose to use local and global variances to augment the vanilla attention model to reproduce the most salient information and avoid repetitions. |
| Outcome: | The proposed attention refinement unit can reproduce the most salient information and avoid repetitions on CNN/Daily Mail dataset. |
Multi-Source Attention for Unsupervised Domain Adaptation (2020.aacl-main)
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| Challenge: | Existing approaches for domain adaptation (UDA) focus on adapting to a domain from a single source domain, but labelled instances are not available for the target domain. |
| Approach: | They propose to model source-selection in unsupervised domain adaptation as an attention-learning problem, where attention is learned over the sources per given target instance. |
| Outcome: | The proposed method outperforms previous proposed methods on two cross-domain sentiment classification datasets and is able to explain the predictions. |
Cheap Character Noise for OCR-Robust Multilingual Embeddings (2025.findings-acl)
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| Challenge: | Optical character recognition (OCR) is a key component of the digitization of historical documents. |
| Approach: | They propose a method that fine-tunes existing multilingual models using noisy texts and a contrastive loss. |
| Outcome: | The proposed model improves on the training data of existing models using noisy texts and a contrastive loss. |
Context-aware Stand-alone Neural Spelling Correction (2020.findings-emnlp)
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| Challenge: | Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. |
| Approach: | They propose a stand-alone spelling correction problem that corrects the spelling of tokens without additional token insertion or deletion. |
| Outcome: | The proposed solution outperforms the state-of-the-art spelling correction model by 12.8% absolute F0.5 score. |