Quranic Optical Text Recognition Using Deep Learning Models

Mohd, Masnizah; Qamar, Faizan; Al-Sheikh, Idris; Salah, Ramzi

doi:10.1109/access.2021.3064019

Cited by 35 publications

(17 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Mohd et al [8] came up with a way to recognize Quranic text using a convolutional neural network (CNN) and a recurrent neural network (RNN). Because they tested it on many data, they found that it had an accuracy rate of 98% on the validation data.…”

Section: Related Workmentioning

confidence: 99%

“…Beyond that, there are challenges inherent in the Arabic language and a dearth of annotated corpora and resources. For the Arabic language, extracting named entities is quite challenging due to its morphological structure [7,8]. Arabic is a morphologically complex language due to its inflectional nature; it has a general form of a word: prefix(es) + stem + suffix(es), with the number of prefixes and suffixes ranging from 0 to many.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

Salah

Mukred

Zakaria

et al. 2022

Journal of Mathematics

Self Cite

View full text Add to dashboard Cite

Named entity recognition (NER) is fundamental in several natural language processing applications. It involves finding and categorizing text into predefined categories such as a person's name, location, and so on. One of the most famous approaches to identify named entity is the rule-based approach. This paper introduces a rule-based NER method that can be used to examine Classical Arabic documents. The proposed method relied on triggers words, patterns, gazetteers, rules, and blacklists generated by the linguistic information about entities named in Arabic. The method operates in three stages, operational stage, preprocessing stage, and processing the rule application stage. The proposed approach was evaluated, and the results indicate that this approach achieved a 90.2% rate of precision, an 89.3% level of recall, and an F-measure of 89.5%. This new approach was introduced to overcome the challenges related to coverage in rule-based NER systems, especially when dealing with Classical Arabic texts. It improved their performance and allowed for automated rule updates. The grammar rules, gazetteers, blacklist, patterns, and trigger words were all integrated into the rule-based system in this way.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

Salah

Mukred

Zakaria

et al. 2022

Journal of Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…21 Deep learning models have been applied to Arabic based scripts with success proving once again they have virtue. [22][23][24] The CNN architectures are widely used in object recognition tasks including character recognition successfully. An important shortcoming of CNN architectures is that they are not good at recognition of sequences of objects such as the sequences of characters in text.…”

Section: Deep Neural Network Architecturementioning

confidence: 99%

A deep learning model for Ottoman OCR

DÖLEK>

Kurt

2022

Concurrency and Computation

View full text Add to dashboard Cite

The Ottoman OCR is an open problem because the OCR models for Arabic do not perform well on Ottoman. The models specifically trained with Ottoman documents have not produced satisfactory results either. We present a deep learning model and an OCR tool using that model for the OCR of printed Ottoman documents in the naksh font. We propose an end-to-end trainable CRNN architecture consisting of CNN, RNN (LSTM), and CTC layers for the Ottoman OCR problem. An experimental comparison of this model, called Osmanlica.com, with the Tesseract Arabic, the Tesseract Persian, Abby Finereader, Miletos, and Google Docs OCR tools or models was performed using a test data set of 21 pages of original documents. With 88.86% raw text, 96.12% normalized text, and 97.37% joined text character recognition accuracy, the Osmanlica.com Hybrid model outperforms the others with a marked difference. Our model outperforms the next best model by a clear margin of 4% which is a significant improvement considering the difficulty of the Ottoman OCR problem, and the huge size of the Ottoman archives to be processed. The hybrid model also achieves 58% word recognition accuracy on normalized text which is the only rate above 50%.

show abstract

“…Currently, the use of gated recurrent unit (GRU) in pattern recognition can give good results. In recognition of Arabic characters, it is recently used by Mohd [8]. The proposed model uses CNN for the extraction of the features and implements BLSTM and BGRU for the recognition phase.…”

Section: Figure 1: Different Shapes Of Arabic Printed Charactersmentioning

confidence: 99%

Improving CNN-BGRU Hybrid Network for Arabic Handwritten Text Recognition

Haboubi¹,

Guesmi²,

Alshammari³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Handwriting recognition is a challenge that interests many researchers around the world. As an exception, handwritten Arabic script has many objectives that remain to be overcome, given its complex form, their number of forms which exceeds 100 and its cursive nature. Over the past few years, good results have been obtained, but with a high cost of memory and execution time. In this paper we propose to improve the capacity of bidirectional gated recurrent unit (BGRU) to recognize Arabic text. The advantages of using BGRUs is the execution time compared to other methods that can have a high success rate but expensive in terms of time and memory. To test the recognition capacity of BGRU, the proposed architecture is composed by 6 convolutional neural network (CNN) blocks for feature extraction and 1 BGRU + 2 dense layers for learning and test. The experiment is carried out on the entire database of institut für nachrichtentechnik/ecole nationale d'ingénieurs de Tunis (IFN/ENIT) without any preprocessing or data selection. The obtained results show the ability of BGRUs to recognize handwritten Arabic script.

show abstract

Quranic Optical Text Recognition Using Deep Learning Models

Cited by 35 publications

References 42 publications

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

A deep learning model for Ottoman OCR

Improving CNN-BGRU Hybrid Network for Arabic Handwritten Text Recognition

Contact Info

Product

Resources

About