Flexible Noisy Text Correction

Sariev, Andrey C.; Nenchev, Vladislav; Mitankin, Petar; Ganchev, Hristo; Mihov, Stoyan; Tinchev, Tinko

doi:10.1109/das.2014.12

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Instead of using the conventional error model, Sariev et al [139] and Niklas [117] deploy word ngram language model with other techniques of candidate generation. Given an erroneous token, Sariev et al [139] generate correction candidates by REBELS [53], the most appropriate one is then selected relying on word bigram/trigrams frequencies. Niklas [117] utilizes the anagram hash algorithm and a new OCR adaptive method to search for the best matching proposals for erroneous OCR tokens.…”

Section: Context-dependent Approachesmentioning

confidence: 99%

Survey of Post-OCR Processing Approaches

et al. 2021

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Optical character recognition (OCR) is one of the most popular techniques used for converting printed documents into machine-readable ones. While OCR engines can do well with modern text, their performance is unfortunately significantly reduced on historical materials. Additionally, many texts have already been processed by various out-of-date digitisation techniques. As a consequence, digitised texts are noisy and need to be post-corrected. This article clarifies the importance of enhancing quality of OCR results by studying their effects on information retrieval and natural language processing applications. We then define the post-OCR processing problem, illustrate its typical pipeline, and review the state-of-the-art post-OCR processing approaches. Evaluation metrics, accessible datasets, language resources, and useful toolkits are also reported. Furthermore, the work identifies the current trend and outlines some research directions of this field.

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Section: Context-dependent Approachesmentioning

confidence: 99%

Survey of Post-OCR Processing Approaches

et al. 2021

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“…The approach takes the whole resulting sentence, and it is also convenient for evaluating the correction of a poor writing style and non-word errors. It was used by Sariev et al [132], Gerdjikov et al [153] and Mitankin et al [131].…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…The machine-translation system is trained on a parallel corpus of examples of spelling errors and corrections. Sariev et al [132] and Koehn et al [152] proposed an ASC system that utilizes a statistical machine-translation system called Moses (http://www.statmt.org/moses/).…”

Section: Spelling Correction As Machine Translation Of Lettersmentioning

confidence: 99%

Survey of Automatic Spelling Correction

2020

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Automatic spelling correction has been receiving sustained research attention. Although each article contains a brief introduction to the topic, there is a lack of work that would summarize the theoretical framework and provide an overview of the approaches developed so far. Our survey selected papers about spelling correction indexed in Scopus and Web of Science from 1991 to 2019. The first group uses a set of rules designed in advance. The second group uses an additional model of context. The third group of automatic spelling correction systems in the survey can adapt its model to the given problem. The summary tables show the application area, language, string metrics, and context model for each system. The survey describes selected approaches in a common theoretical framework based on Shannon’s noisy channel. A separate section describes evaluation methods and benchmarks.

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“…This set is freely available and has already been used to evaluate OCR spelling correction system (e.g. in [8,25]). The database contains original text document and text output degraded by an OCR system.…”

Section: Methodsmentioning

confidence: 99%

Learning string distance with smoothing for OCR spelling correction

Hládek

Staš

Ondáš

et al. 2016

Multimed Tools Appl

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Large databases of scanned documents (medical records, legal texts, historical documents) require natural language processing for retrieval and structured information extraction. Errors caused by the optical character recognition (OCR) system increase ambiguity of recognized text and decrease performance of natural language processing. The paper proposes OCR post correction system with parametrized string distance metric. The correction system learns specific error patterns from incorrect words and common sequences of correct words. A smoothing technique is proposed to assign non-zero probability to edit operations not present in the training corpus. Spelling correction accuracy is measured on database of OCR legal documents in English language. Language model and learning string metric with smoothing improves Viterbi-based search for the best sequence of corrections and increases performance of the spelling correction system.

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Flexible Noisy Text Correction

Cited by 5 publications

References 14 publications

Survey of Post-OCR Processing Approaches

Survey of Post-OCR Processing Approaches

Survey of Automatic Spelling Correction

Learning string distance with smoothing for OCR spelling correction

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