Handwritten Arabic Character Recognition: Comparison of Conventional Machine Learning and Deep Learning Approaches

Faouci, Soumia; Gaceb, Djamel; Haddad, Mohammed

doi:10.1007/978-3-030-70713-2_100

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…However, it achieved the second-best accuracy on the AHCD dataset. [32] 2017 94.90 [45] 2017 94.8 [34] 2021 92.5 [45] 2017 97.60 [44] 2014 94.28 [36] 2022 91.2 [46] 2021 98.21 [47] 2021 93.30 [37] 2022 91.0 [34] 2021 95.4 [48] 2021 95.20 This paper 2024 93.05 [43] 2021 97.00 [39] 2022 92.91 [49] 2022 96.78 [40] 2023 95.00 [36] 2022 98.48 This paper 2024 96.88 [37] 2022 98.00 [38] 2024 95.89 This paper 2024 98.30 6…”

Section: Comparison With Existing Approachesmentioning

confidence: 99%

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Alghyaline

2024

CMC

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Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles. Arabic is morphologically rich, and its characters have a high similarity. The Arabic language includes 28 characters. Each character has up to four shapes according to its location in the word (at the beginning, middle, end, and isolated). This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters. The proposed architectures were derived from the popular CNN architectures, such as VGG, ResNet, and Inception, to make them applicable to recognizing character-size images. The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models. The experiments showed that data augmentation improved the models' accuracies on all tested datasets. The proposed model outperformed most of the existing approaches. The best achieved results were 93.05%, 98.30%, and 96.88% on the HIJJA, AHCD, and AIA9K datasets.

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Section: Comparison With Existing Approachesmentioning

confidence: 99%

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Alghyaline

2024

CMC

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“…In [5], Soumia et al compared two approaches to Arabic handwritten character recognition. The first uses conventional machine learning with the SVM classifier.…”

Section: Cnn In Arabic Handwritten Recognitionmentioning

confidence: 99%

“…Handwritten text for each language has many different patterns and styles from writer to writer, affected by factors such as age, background, native language, and mental state [2]. Automatic handwritten recognition has been extensively investigated using various machine learning methods, including K-nearest Neighbors (KNNs) [3], Support Vector Machines (SVMs), transfer learning [4,5], and deep learning techniques such as Neural Networks (NNs) [6]. Recently, most studies have emplyed Convolutional Neural Networks (CNNs) [5,[7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Automatic handwritten recognition has been extensively investigated using various machine learning methods, including K-nearest Neighbors (KNNs) [3], Support Vector Machines (SVMs), transfer learning [4,5], and deep learning techniques such as Neural Networks (NNs) [6]. Recently, most studies have emplyed Convolutional Neural Networks (CNNs) [5,[7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Handwritten Arabic Character Recognition for Children Writing Using Convolutional Neural Network and Stroke Identification

Mais

Al-Matham

Al‐Khalifa

2023

Hum-Cent Intell Syst

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Automatic Arabic handwritten recognition is one of the recently studied problems in the field of Machine Learning. Unlike Latin languages, Arabic is a Semitic language that forms a harder challenge, especially with the variability of patterns caused by factors such as the writer’s age. Most studies have focused on adults, with only one recent study on children. Moreover, many recent machine-learning methods have focused on using Convolutional Neural Networks (CNNs), a powerful class of neural networks that can extract complex features from images. In this paper, we propose a convolutional neural network (CNN) model that recognizes children’s handwriting with an accuracy of 91% on the Hijja dataset, a recent dataset built by collecting images of Arabic characters written by children, and 97% on the Arabic Handwritten Character Dataset. The results showed a good improvement over the proposed model from the Hijja dataset authors, yet it revealed a bigger challenge to solve for children’s Arabic handwritten character recognition. Moreover, we proposed a new approach using multiple models instead of a single model based on the number of strokes in a character and merged Hijja with AHCD, which achieved an average prediction accuracy of 96%.

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Handwritten Arabic Character Recognition: Comparison of Conventional Machine Learning and Deep Learning Approaches

Cited by 2 publications

References 17 publications

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Handwritten Arabic Character Recognition for Children Writing Using Convolutional Neural Network and Stroke Identification

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