Arabic Handwritten Recognition Using Deep Learning: A Survey

Alrobah, Naseem; Albahli, Saleh

doi:10.1007/s13369-021-06363-3

Cited by 30 publications

(21 citation statements)

References 58 publications

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“…Deep learning models such as CNN, RNN, and attention-based models are discussed in [94]. The paper discusses the performance of models on different Arabic handwritten datasets, and these models show much-improved character recognition rate, word recognition rate, and overall recognition rate.…”

Section: Preprocessingmentioning

confidence: 99%

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

et al. 2023

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Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.

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Section: Preprocessingmentioning

confidence: 99%

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

et al. 2023

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“…Meanwhile, the more uniform strokes in length and discrete letters were the main dependent features of Hebrew script recognition. According to the latest survey for Arabic recognition systems [31], the highest accuracy score is 99.98%, while recorded 97.15% for Hebrew [32]. In Farsi, Urdu, and Uyghur, the highest accuracies achieved are 99.45%, 98.82%, and 93.94%, respectively [33]- [35].…”

Section: Abjadsmentioning

confidence: 99%

Construction of Alphabetic Character Recognition Systems: A Review

Majeed

Nariman

2023

UHD J SCI TECH

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Character recognition (CR) systems were attracted by a massive number of authors’ interest in this field, and lot of research has been proposed, developed, and published in this regard with different algorithms and techniques due to the great interest and demand of raising the accuracy of the recognition rate and the reliability of the presented system. This work is proposed to provide a guideline for CR system construction to afford a clear view to the authors on building their systems. All the required phases and steps have been listed and clarified within sections and subsections along with detailed graphs and tables beside the possibilities of techniques and algorithms that might be used, developed, or merged to create a high-performance recognition system. This guideline also could be useful for readers interested in this field by helping them extract the information from such papers easily and efficiently to reach the main structure along with the differences between the systems. In addition, this work recommends to researchers in this field to comprehend a specified categorical table in their work to provide readers with the main structure of their work that shows the proposed system’s structural layout and enables them to easily find the information and interests.

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“…Some characteristics of Arabic script, such as being written in cursive fashion, changing shape depending on their position within the word, and omission of the vowels constitute the main difficulties for Arabic OCR. As with other languages, while HMM-based systems have been most popular prior to deep learning era [4,34,42], they been gradually replaced by deep learning methods in recent years [9,10]). There are also studies in which HMM and Artificial Neural Networks (ANN) techniques are used together as in the study conducted by Rahal et al [45] where a Bag-of-Feature (BoF) framework based on a deep Sparse Auto-Encoder (SAE) is employed for feature extraction and HMMs are used for sequence recognition.…”

Section: Related Workmentioning

confidence: 99%

Transcription of Ottoman Machine-Print Documents

Tasdemir

Kizilirmak

Akcan

et al. 2022

Preprint

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With the ever increasing speed of the digitization process, a large collection of Ottoman documents is accessible to researchers and the general public. But, the majority of the users interested in these documents can not read these documents unless they are transcripted to the modern Turkish script which use an extended version of the Latin alphabet. Manual transcription of such a massive amount of documents is beyond the capacity of human experts. As a solution, we propose an automatic recognition system for printed Ottoman documents which transcribes Ottoman texts directly to the modern Turkish script. We evaluated three decoding strategies including the Word Beam Search decoder that allows to use a recognition lexicon and n-gram statistics during the decoding phase. The system achieves 2.25% character error rate and 6.42% word error rate on a test set of 1.4K samples, using the test set transcriptions as the recognition lexicon. Using a general purpose, large lexicon of the Ottoman era (260K words and 77% test coverage), the performance is measured as 3.68% character error rate and 16.61% word error rate.

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Arabic Handwritten Recognition Using Deep Learning: A Survey

Cited by 30 publications

References 58 publications

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

Construction of Alphabetic Character Recognition Systems: A Review

Transcription of Ottoman Machine-Print Documents

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