Deep Learning Based Handwritten Chinese Character and Text Recognition

Zhang, Xu-Yao; Wu, Yichao; Yin, Fei; Liu, Cheng‐Lin

doi:10.1007/978-3-030-06073-2_3

Cited by 4 publications

(2 citation statements)

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“…Zhang et al [6], introduced new advances on implementing deep learning methods for handwritten Chinese character recognition and handwritten Chinese text recognition. They eliminated the need for data augmentation and model ensemble.…”

Section: Previous Workmentioning

confidence: 99%

Tifinagh handwritten character recognition using optimized convolutional neural network

Niharmine

Outtaj

Azouaoui

2022

IJECE

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<p><span>Tifinagh handwritten character recognition has been a challenging problem due to the similarity and variability of its alphabets. This paper proposes an optimized convolutional neural network (CNN) architecture for handwritten character recognition. The suggested model of CNN has a multi-layer feed-forward neural network that gets features and properties directly from the input data images. It is based on the newest deep learning open-source Keras Python library. The novelty of the model is to optimize the optical character recognition (OCR) system in order to obtain best performance results in terms of accuracy and execution time. The new optical character recognition system is tested on a customized dataset generated from the amazigh handwritten character database. Experimental results show a good accuracy of the system (99.27%) with an optimal execution time of the classification compared to the previous works.</span></p>

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Section: Previous Workmentioning

confidence: 99%

Tifinagh handwritten character recognition using optimized convolutional neural network

Niharmine

Outtaj

Azouaoui

2022

IJECE

View full text Add to dashboard Cite

show abstract

“…They are mentioned here to prove the large demand for data if we use character-based recognition. Nevertheless, they are not flexible enough for various circumstances, e.g., handwritten text [37]. And current methods in that way cost much time in practice due to the use of RNN-based decoder.…”

Section: Introductionmentioning

confidence: 99%

Maximum Entropy Regularization and Chinese Text Recognition

Cheng

Bai

et al. 2020

Preprint

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Chinese text recognition is more challenging than Latin text due to the large amount of fine-grained Chinese characters and the great imbalance over classes, which causes a serious overfitting problem. We propose to apply Maximum Entropy Regularization to regularize the training process, which is to simply add a negative entropy term to the canonical cross-entropy loss without any additional parameters and modification of a model. We theoretically give the convergence probability distribution and analyze how the regularization influence the learning process. Experiments on Chinese character recognition, Chinese text line recognition and fine-grained image classification achieve consistent improvement, proving that the regularization is beneficial to generalization and robustness of a recognition model.

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Maximum Entropy Regularization and Chinese Text Recognition

Cheng

Bai

et al. 2020

Lecture Notes in Computer Science

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Deep Learning Based Handwritten Chinese Character and Text Recognition

Cited by 4 publications

References 42 publications

Tifinagh handwritten character recognition using optimized convolutional neural network

Tifinagh handwritten character recognition using optimized convolutional neural network

Maximum Entropy Regularization and Chinese Text Recognition

Maximum Entropy Regularization and Chinese Text Recognition

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