Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods (L18-1)
Copied to clipboard
| Challenge: | Optical Character Recognition (OCR) can produce a range of errors depending on the quality of the original document. |
| Approach: | They applied a sequence-to-sequence machine translation system to correct word-single-word OCR errors in scientific texts from the ACL collection with an estimated precision and recall above 0.95 on test data. |
| Outcome: | The proposed system corrects word-segmentation OCR errors with an estimated precision and recall above 0.95 on test data. |
Similar Papers
Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling (2021.findings-acl)
Copied to clipboard
| Challenge: | Standard sequence labeling systems fail when processing noisy user-generated text or consuming the output of an OCR process. |
| Approach: | They propose an empirical error generation approach that employs a sequence-to-sequence model trained to perform translation from error-free to erroneous text. |
| Outcome: | The proposed method outperforms baseline noise generation and error correction techniques on the erroneous sequence labeling data sets. |
Unsupervised Multi-View Post-OCR Error Correction With Language Models (2021.emnlp-main)
Copied to clipboard
| 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. |
LOCR: Location-Guided Transformer for Optical Character Recognition (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Academic documents are packed with texts, equations, tables, and figures, posing challenges for accurate OCR results. |
| Approach: | They propose a model that integrates location guiding into the transformer architecture during autoregression. |
| Outcome: | The proposed model outperforms existing methods on an original large-scale dataset comprising 53M text-location pairs from 89K academic document pages. |
Cleaning Dirty Books: Post-OCR Processing for Previously Scanned Texts (2021.findings-emnlp)
Copied to clipboard
| Challenge: | a large amount of work is required to clean digitized books for NLP analysis because of errors in the scanned text and duplicate volumes in the corpora. |
| Approach: | They propose methods to handle optical character recognition errors in scanned texts . they identify the canonical version for each of 17,136 repeatedly-scanned books . |
| Outcome: | The proposed method corrects over six times as many errors as it introduces, the authors show . the authors evaluate a collection of 19,347 texts from the Gutenberg dataset and 96,635 from the HathiTrust Library . |
Neural OCR Post-Hoc Correction of Historical Corpora (2021.tacl-1)
Copied to clipboard
| 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. |
In-Image Neural Machine Translation with Segmented Pixel Sequence-to-Sequence Model (2023.findings-emnlp)
Copied to clipboard
| Challenge: | In-Image Machine Translation (IIMT) aims to convert images containing texts from one language to another. |
| Approach: | They propose an end-to-end model instead of the traditional cascade methods which use optical character recognition followed by neural machine translation and text rendering. |
| Outcome: | The proposed model outperforms both cascade methods and current model in translation quality and robustness across various dimensions. |
Efficient OCR for Building a Diverse Digital History (2024.acl-long)
Copied to clipboard
| Challenge: | Current optical character recognition (OCR) systems are poorly extensible to low-resource document collections, as learning a language-vision model requires extensive labeled sequences and compute. |
| Approach: | They propose to model optical character recognition as a character level image retrieval problem using a contrastively trained vision encoder. |
| Outcome: | The proposed model is more sample efficient and extensible than existing architectures, enabling accurate OCR in settings where existing solutions fail. |
Low-resource Post Processing of Noisy OCR Output for Historical Corpus Digitisation (L18-1)
Copied to clipboard
| 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. |
OCR Improves Machine Translation for Low-Resource Languages (2022.findings-acl)
Copied to clipboard
| Challenge: | Despite many recent successes, Machine Translation still lacks support or fails to achieve good performance for most low-resource languages. |
| Approach: | They propose a benchmark to evaluate OCR systems on low-resource languages and low- resource scripts. |
| Outcome: | The proposed benchmark evaluates state-of-the-art OCR systems on low-resource languages and low-rural scripts. |
Automatic Correction of Human Translations (2022.naacl-main)
Copied to clipboard
| Challenge: | Despite recent advances in machine translation, a tremendous amount of translated content in the world is still written by humans. |
| Approach: | They propose a task of translation error correction (TEC) that corrects human-generated translations by correcting all errors in a source sentence and a human-created translation. |
| Outcome: | The proposed system improves translation accuracy by 5.1 points compared to MT systems with human errors . |