Printed Arabic Text Recognition using Linear and Nonlinear Regression

Shahin, Ashraf A.

doi:10.14569/ijacsa.2017.080129

Cited by 5 publications

(4 citation statements)

References 15 publications

(20 reference statements)

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“…In this testing scenario, the model achieved good results in CRR (99.27%) but it demonstrated a minor degradation in WRR (94.32%). This behavior can be 3 https://github.com/JTCodeStore/ArabicDLOCR justified by emphasizing that the WRR is highly dependent on the accuracy of the CRR, and a minor flaw in the recognition of a single character shall affect the recognition of all the related words, especially that the dataset samples included relatively long words (a length of 7-10 characters).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The recognition of Arabic text is still a challenging task because of many intricate features related to the nature of Arabic writing system [3]. Work in this domain is an active research area where many models are continuously proposed for the problem of automatically recognizing printed or handwritten text.…”

Section: Related Workmentioning

confidence: 99%

“…The KNN classifier achieved a successful rate of (98%), while the RFT classifier achieved (87%). Similarly, the authors in [3] introduced a model for recognizing Arabic printed text using linear and nonlinear regression. In that work, text in images were initially thinned and segmented into sub words.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Deep Learning Model for Arabic Text Recognition

Fasha¹,

Hammo²,

Obeid³

et al. 2020

IJACSA

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Arabic text recognition is a challenging task because of the cursive nature of Arabic writing system, its joint writing scheme, the large number of ligatures and many other challenges. Deep Learning (DL) models achieved significant progress in numerous domains including computer vision and sequence modelling. This paper presents a model that can recognize Arabic text that was printed using multiple font types including fonts that mimic Arabic handwritten scripts. The proposed model employs a hybrid DL network that can recognize Arabic printed text without the need for character segmentation. The model was tested on a custom dataset comprised of over two million word samples that were generated using (18) different Arabic font types. The objective of the testing process was to assess the model's capability in recognizing a diverse set of Arabic fonts representing a varied cursive styles. The model achieved good results in recognizing characters and words and it also achieved promising results in recognizing characters when it was tested on unseen data. The prepared model, the custom datasets and the toolkit for generating similar datasets are made publically available, these tools can be used to prepare models for recognizing other font types as well as to further extend and enhance the performance of the proposed model.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Hybrid Deep Learning Model for Arabic Text Recognition

Fasha¹,

Hammo²,

Obeid³

et al. 2020

IJACSA

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“…The purpose of that work was to evaluate the results of extending the dataset using data enhancing techniques and compare the performance of an extended model with other related models. The dataset used for training and evaluation is handwritten KHAT [37]. The model achieved 80.02% validation accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

et al. 2021

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According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic language per some analytical information. Many countries have Arabic as their native and official language as well. In recent years, the number of internet users speaking the Arabic language has been increased, but there is very little work on it due to some complications. It is challenging to build a robust recognition system (RS) for cursive nature languages such as Arabic. These challenges become more complex if there are variations in text size, fonts, colors, orientation, lighting conditions, noise within a dataset, etc. To deal with them, deep learning models show noticeable results on data modeling and can handle large datasets. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can select good features and follow the sequential data learning technique. These two neural networks offer impressive results in many research areas such as text recognition, voice recognition, several tasks of Natural Language Processing (NLP), and others. This paper presents a CNN-RNN model with an attention mechanism for Arabic image text recognition. The model takes an input image and generates feature sequences through a CNN. These sequences are transferred to a bidirectional RNN to obtain feature sequences in order. The bidirectional RNN can miss some preprocessing of text segmentation. Therefore, a bidirectional RNN with an attention mechanism is used to generate output, enabling the model to select relevant information from the feature sequences. An attention mechanism implements end-to-end training through a standard backpropagation algorithm.

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