A novel Arabic OCR post-processing using rule-based and word context techniques

Doush, Iyad Abu; Alkhateeb, Faisal; Gharaibeh, Anwaar Hamdi

doi:10.1007/s10032-018-0297-y

Cited by 19 publications

(13 citation statements)

References 30 publications

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“…Sonia Yousf et al (2017) [19] presented improving Long-Short Term Memory (LSTM) of AOCR of text in videos by recurrent connections language modelling by focusing on two factors Recurrent Neural Network (RNN) for language modelling and decoding schema. Doush, Alkhateeb, and Hamdi (2018) [20] Proposed model of language-independent a AOCR post-processing system by two frameworks the Language model and hybrid error model with contextual model.…”

Section: Related Workmentioning

confidence: 99%

Corpus-based technique for improving Arabic OCR system

Aliwy

Al-Sadawi

2021

IJEECS

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<p><span>An optical character recognition (OCR) refers to a process of converting the text document images into editable and searchable text. OCR process poses several challenges in particular in the Arabic language due to it has caused a high percentage of errors. In this paper, a method, to improve the outputs of the Arabic Optical character recognition (AOCR) Systems is suggested based on a statistical language model built from the available huge corpora. This method includes detecting and correcting non-word and real words error according to the context of the word in the sentence. The results show that the percentage of improvement in the results is up to (98%) as a new accuracy for AOCR output. </span></p>

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Section: Related Workmentioning

confidence: 99%

Corpus-based technique for improving Arabic OCR system

Aliwy

Al-Sadawi

2021

IJEECS

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“…The study of post-processing on the OCR results in the two latter categories (dictionary-based and context-based categories) have been done for several different languages (Nagata in Japanese [ 27], Afli et al in French [ 28], Kesorn et al in Thai [ 29], Abu Douch et al [ 30] and Magdy et al [ 31] in Arabic, Ramanan et al in Tamil [ 32] ,Kolak et al [ 33] in Spanish, Igbo, Cebuano, and Arabic). As an example of the OCR post-processing in the Arabic language, Zaiz et al [ 34] improved the Arabic OCR results by proposing a post-processing technique that was working based on a support vector machine (SVM) classifier and a Puzzle algorithm.…”

Section: Context-based Error Post-processing Approachesmentioning

confidence: 99%

A Rule-Based Post-Processing Approach to Improve Persian OCR Performance

et al. 2020

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Optical Character Recognition (OCR) is a system to convert images including text into an editable text. Nowadays, the accuracy of these systems in images with simple-structure and high quality is high. However, the performance degrades for images with complex-structure, low quality, and in the presence of noise, scratches, pictures, stamps, or other non-textual symbols. This paper proposes a Persian OCR post-processing technique to increase the accuracy of the OCR systems dealing with real-world challenging samples. The proposed method extracts five features in each line of the text and uses seven proposed rules to investigate whether that line should be ignored or not. To evaluate the proposed method, Khana (structural based) and Bina (deep learning-based) Persian OCR systems have utilized, and a dataset containing 200 complex-structure images has been collected and a dataset including 100 simple-structure images have been used. The accuracy of the Khana and Bina in images with complex-structure is 39% and 58%, respectively, while after applying the proposed post-processing method the accuracy increases to 93% and 91%, respectively.

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“…Different techniques have been proposed for OCR postprocessing such as manual error correction, dictionary (or lexical) based error correction and context-based error correction [2]- [7]. Manual error correction of OCR output is time-consuming and error-prone.…”

Section: Related Workmentioning

confidence: 99%

On the Use of Neural Text Generation for the Task of Optical Character Recognition

Mohammadi

Jaf

McGough

et al. 2019

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)

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Optical Character Recognition (OCR), is extraction of textual data from scanned text documents to facilitate their indexing, searching, editing and to reduce storage space. Although OCR systems have improved significantly in recent years, they still suffer in situations where the OCR output does not match the text in the original document. Deep learning models have contributed positively to many problems but their full potential to many other problems are yet to be explored. In this paper we propose a post-processing approach based on the application deep learning to improve the accuracy of OCR system (minimizing the error rate). We report on the use of neural network language models to accomplish the task of correcting incorrectly predicted characters/words by OCR systems. We applied our approach to the IAM handwriting database. Our proposed approach delivers significant accuracy improvement of 20.41% in F-score, 10.86% in character level comparison using Levenshtein distance and 20.69% in document level comparison over previously reported context based OCR empirical results of IAM handwriting database.

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A novel Arabic OCR post-processing using rule-based and word context techniques

Cited by 19 publications

References 30 publications

Corpus-based technique for improving Arabic OCR system

Corpus-based technique for improving Arabic OCR system

A Rule-Based Post-Processing Approach to Improve Persian OCR Performance

On the Use of Neural Text Generation for the Task of Optical Character Recognition

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