Automatic Building of a Large Arabic Spelling Error Corpus

Aichaoui, Shaimaa Ben; Hiri, Nawel; Dahou, Abdelhalim Hafedh; Cheragui, Mohamed Amine

doi:10.1007/s42979-022-01499-x

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Aichaoui et al [61] created a large dataset of spelling errors, called SPIRAL, for use as data for training deep-learning models that aim to fix spelling errors due to the lack of such a large set in Arabic. They collected textual data from various newspaper sites, such as Okaz and available open Arabic corpora sites, such as Maktabah Shamlah.…”

Section: Post-ocr Correction Work [61-71]mentioning

confidence: 99%

“…In addition, GANs must be combined with a classifier, which contributes to enhancing the accuracy on one hand and the model's complexity on the other; • There is a crucial need for more public handwritten Arabic OCR datasets in terms of quantity, quality, scope, and font diversity in order for OCR research to progress. One significant limitation of the work of Aichaoui et al [61] is that the corpus building has an obviously uneven distribution between the types and categories of error; therefore, a lack of accuracy and results is associated with low-frequency errors. Evidence of this is shown as the numbers of deletion and keyboard errors (which obtained the worst recall, at 0.337 and 0.644, respectively) were, in fact, among the lowest in the dataset, comprising 0.67% and 6.26%, respectively.…”

Section: Gans-based Work [57-60]mentioning

confidence: 99%

“…Aichaoui et al [61] combined AraBART with the SPIRAL dataset, which has eight kinds of error for training and testing. The best recall was reportedly 0.863, and it was for space-issued errors.…”

Section: Robust Ocr and Text-correction Techniquesmentioning

confidence: 99%

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Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction

Najam

Faizullah

2023

Applied Sciences

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Arabic handwritten-text recognition applies an OCR technique and then a text-correction technique to extract the text within an image correctly. Deep learning is a current paradigm utilized in OCR techniques. However, no study investigated or critically analyzed recent deep-learning techniques used for Arabic handwritten OCR and text correction during the period of 2020–2023. This analysis fills this noticeable gap in the literature, uncovering recent developments and their limitations for researchers, practitioners, and interested readers. The results reveal that CNN-LSTM-CTC is the most suitable architecture among Transformer and GANs for OCR because it is less complex and can hold long textual dependencies. For OCR text correction, applying DL models to generated errors in datasets improved accuracy in many works. In conclusion, Arabic OCR has the potential to further apply several text-embedding models to correct the resultant text from the OCR, and there is a significant gap in studies investigating this problem. In addition, there is a need for more high-quality and domain-specific OCR Arabic handwritten datasets. Moreover, we recommend the practical development of a space for future trends in Arabic OCR applications, derived from current limitations in Arabic OCR works and from applications in other languages; this will involve a plethora of possibilities that have not been effectively researched at the time of writing.

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Section: Post-ocr Correction Work [61-71]mentioning

confidence: 99%

Section: Gans-based Work [57-60]mentioning

confidence: 99%

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Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction

Najam

Faizullah

2023

Applied Sciences

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SPECIL: Spell Error Corpus for the Indonesian Language

Yanfi,

Setiawan,

Soeparno

et al. 2023

IEEE Access

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In this study, we present the first spell error corpus for the Indonesian Language (SPECIL). This corpus provides a comprehensive resource for researchers and practitioners to detect and correct spelling errors in Bahasa Indonesia (Indonesian). It should be emphasized that currently, there is no recognized corpus for identifying spelling mistakes in the Indonesian language that has been officially released or made accessible. This study also provides a systematic literature review to identify resources and methodologies for building a corpus for spelling error detection and correction in Indonesia. A corpus was created using a combination of manual and automatic methods. The results of this study are a review of publications relating to corpora and spelling, the novel algorithm of six types of spelling errors, and the production of a corpus comprising over 180,000 tokens in 21,500 sentences, including non-word, real-word, and punctuation errors. Using the developed corpus, various Natural Language Processing (NLP) models, including spell checkers and language models, can be trained and tested to enhance their accuracy and effectiveness in identifying and rectifying errors in Indonesian texts. Moreover, the corpus can be used to develop and evaluate new algorithms and techniques for spelling error detection and correction in Indonesia. The SPECIL corpus is publicly available and accessible. It is expected that SPECIL will inspire further research in this area and facilitate the development of more accurate and effective spelling error detection and correction tools in Indonesian language.

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