Bayesian Versus Convolutional Networks for Arabic Handwriting Recognition

Khémiri, Akram; Echi, Afef Kacem; Elloumi, Mourad

doi:10.1007/s13369-019-03939-y

Cited by 15 publications

(6 citation statements)

References 27 publications

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“…Architecture Accuracy [52] MDLSTM-CTC 88.38% [29] CNN-SVM 92.95% [39] Dynamic Bayesian Network 82% [8] CNN-HMM 89.23% [11] HMM(128 Mixtures) 93% [53] CNN-BLSTM 92.21% [36] ANN 87.10% [1] RNN 94.45% [30] DBN + CDBN 96.23% [52] MDLSTM with dropout 94.65% [27] CNN 94.9% [43] Bayesian + CNN 95.2% [61] CNN + RNN 96.75% Proposed Method CNN + Att-BLSTM + CTC + dropout 97.1%…”

Section: Methodsmentioning

confidence: 99%

Attention-based deep learning model for Arabic handwritten text recognition

Gader

Echi

2022

MG&V

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This work proposes a segmentation-free approach to Arabic Handwritten Text Recognition (AHTR): an attention-based Convolutional Neural Network - Recurrent Neural Network - Connectionist Temporal Classification (CNN-RNN-CTC) deep learning architecture. The model receives as input an image and provides, through a CNN, a sequence of essential features, which are transferred to an Attention-based Bidirectional Long Short-Term Memory Network (BLSTM). The BLSTM gives features sequence in order, and the attention mechanism allows the selection of relevant information from the features sequences. The selected information is then fed to the CTC, enabling the loss calculation and the transcription prediction. The contribution lies in extending the CNN by dropout layers, batch normalization, and dropout regularization parameters to prevent over-fitting. The output of the RNN block is passed through an attention mechanism to utilize the most relevant parts of the input sequence in a flexible manner. This solution enhances previous methods by improving the CNN speed and performance and controlling over model over-fitting. The proposed system achieves the best accuracy of 97.1% for the IFN-ENIT Arabic script database, which competes with the current state-of-the-art. It was also tested for the modern English handwriting of the IAM database, and the Character Error Rate of 2.9% is attained, which confirms the model's script independence.

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Section: Methodsmentioning

confidence: 99%

Attention-based deep learning model for Arabic handwritten text recognition

Gader

Echi

2022

MG&V

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“…Khemiri et al [90] hybridized CNN with another type of sequenced data learning ANN, specifically Dynamic Bayesian Network (DBN), this time utilizing the ANN for feature extraction. DBN, renowned for its adeptness in extracting representative features, was leveraged to combine the advantages of handcrafted features extracted by DBN and machinelearned features of CNN.…”

Section: Word Recognitionmentioning

confidence: 99%

CNN-based Methods for Offline Arabic Handwriting Recognition: A Review

El Khayati,

Kich,

Taouil

2024

Neural Process Lett

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Arabic Handwriting Recognition (AHR) is a complex task involving the transformation of handwritten Arabic text from image format into machine-readable data, holding immense potential across various applications. Despite its significance, AHR encounters formidable challenges due to the intricate nature of Arabic script and the diverse array of handwriting styles. In recent years, Convolutional Neural Networks (CNNs) have emerged as a pivotal and promising solution to address these challenges, demonstrating remarkable performance and offering distinct advantages. However, the dominance of CNNs in AHR lacks a dedicated comprehensive review in the existing literature. This review article aims to bridge the existing gap by providing a comprehensive analysis of CNN-based methods in AHR. It covers both segmentation and recognition tasks, delving into advancements in network architectures, databases, training strategies, and employed methods. The article offers an in-depth comparison of these methods, considering their respective strengths and limitations. The findings of this review not only contribute to the current understanding of CNN applications in AHR but also pave the way for future research directions and improved practices, thereby enriching and advancing this critical domain. The review also aims to uncover genuine challenges in the domain, providing valuable insights for researchers and practitioners.

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“…Most of the false recognition are in the class which presents <10 samples. CNN 97.07% [34] multi-column deep neural network (MCDNN) 91.50% [35] deep belief network (DBN) 94.99% [36] CNN-DBN 95.20% [37] AlexNet 95.60% Our BGRU-CNN 86.78%-100% Number of samples in the learning phase is a very interesting factor for the success of an intelligent system. This is also proved by our proposed system.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Improving CNN-BGRU Hybrid Network for Arabic Handwritten Text Recognition

Haboubi¹,

Guesmi²,

Alshammari³

et al. 2022

Computers, Materials &Amp; Continua

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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.

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Bayesian Versus Convolutional Networks for Arabic Handwriting Recognition

Cited by 15 publications

References 27 publications

Attention-based deep learning model for Arabic handwritten text recognition

Attention-based deep learning model for Arabic handwritten text recognition

CNN-based Methods for Offline Arabic Handwriting Recognition: A Review

Improving CNN-BGRU Hybrid Network for Arabic Handwritten Text Recognition

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