Multi-layer recurrent neural network based offline Arabic handwriting recognition

Chen, Liren; Yan, Ruijie; Peng, Liangrui; Furuhata, Akio; Ding, Xiaoqing

doi:10.1109/asar.2017.8067749

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…In 2017, Chen et al [17] presented a segmentation-free approach of RNN with a fourlayer bidirectional Gated Recurrent Unit (GRU) network with a CTC output layer and combined it with the dropout technique. The authors evaluated the system performance on the IFN/ENIT database with the "abcd-e" scenario.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

Attention-based deep learning model for Arabic handwritten text recognition

Gader

Echi

2022

MG&V

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“…Chen et al [ 76 ] provided a segmentation-free method of RNN with a four-layer bidirectional Gated Recurrent Unit (GRU) network along with a Connectionist Temporal Classification (CTC) output layer and combined it with the dropout technique, which was claimed to improve the system’s generalization ability. The RRN-GRU was designed to identify words in offline handwritten Arabic.…”

Section: Related Workmentioning

confidence: 99%

“…The CNN model can be exploited in two different ways: either by designing the model manually or automatically and then achieving the training process from scratch [ 59 , 60 , 61 , 62 ] or by employing the Transfer Learning (TL) strategies to leverage features from off-the-shelf pre-trained models on bigger databases [ 30 , 63 ]. There are many available state-of-the-arts pre-trained CNN models that have been trained on the ImageNet database [ 64 ], such as VGGNet [ 65 ], GoogLeNet [ 66 ], ResNet [ 67 ], InceptionV3 [ 68 ], Xception [ 69 ], MobileNet [ 70 ], and DenseNet [ 71 ].The TL technique is used to transfer the acquired knowledge from one or more tasks in the source domain to another task in the target domain [ 28 , 63 , 72 , 73 , 74 , 75 ] by utilizing a pre-trained network from a source domain that has a considerable amount of training data [ 74 , 76 ], and this helps in boosting the recognition accuracy or reducing training time [ 74 ]. The two widely utilized TL strategies are the feature extraction strategy from prior trained data and the fine-tuning strategy of the applied pre-trained network [ 63 , 77 ].…”

Section: Introductionmentioning

confidence: 99%

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Ahmed

Gogate

Tahir

et al. 2021

Entropy

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Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model.

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“…Anne et al [129] proposed word recognition technique based on HMM. Liren et al [130] proposed segmentation-free approach which used RNN. These networks are based on deep learning concept and used four-bidirectional gated recurrent units with dropout layer for generalisation.…”

Section: Comparison With Other Languagesmentioning

confidence: 99%