Application of Convolutional Neural Network in Handwritten Chinese Character Recognition

Guo, Hongliang; Ai, Likun; Chen, Shuxin

doi:10.1109/iciba50161.2020.9277290

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Momentum SGD optimization is used by CNN. This study tries to reduce model error by employing a small momentum decay factor [ 14 ], the literature's initialization approach [ 15 ], and the same batch normalization [ 16 ]. Samples are shuffled before being used to create a training set, followed by validation and testing on the new samples in the set.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model

Thakare¹,

Bhende²,

Deb

et al. 2022

BioMed Research International

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Patients suffering from severe depression may be precisely assessed using online EEG categorization and their progress tracked over time, minimizing the risk of danger and suicide. Online EEG categorization systems, on the other hand, suffer additional challenges in the absence of empirical oversight. A lack of effective decoupling between brain regions and neural networks occurs during brain disease attacks, resulting in EEG data with poor signal intensity, high noise, and nonstationary characteristics. CNN employs momentum SGD optimization. By using a tiny momentum decay factor, the literature’s starting strategy, and the same batch normalization, this work attempts to decrease model error. Before being utilized to form a training set, samples are shuffled, followed by validation and testing on the new samples in the set. An online EEG categorization system driven by a convolution neural network has been developed to do this. The approach is applied directly to the EEG input and is able to accurately and quickly identify depressed states without the need for preprocessing or feature extraction. The healthy control group and the depression control group had accuracy, sensitivity, and specificity of 99.08 percent, 98.77 percent, and 99.42 percent, respectively, in experiments on depression evaluation based on publicly accessible data. The machine learning technique based on feature extraction is often getting more and more complex, making it only suited for offline EEG categorization. While neural networks have become increasingly important in the study of artificial intelligence in recent years, they are still essentially black-box function approximations with limited interpretability. In addition, quantitative study of the neural network shows that depressed patients and healthy persons have remarkable dissimilarity between the right and left temporal lobe brain regions.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model

Thakare¹,

Bhende²,

Deb

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

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Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods

Zhong,

Daud,

Nor

et al. 2023

JACIII

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Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics’ interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. However, the recognition accuracy of offline HCCR is still a sophisticated challenge owing to their complexity and variety of writing styles. With the advancement of deep learning, convolutional neural network (CNN)-based algorithms have demonstrated distinct benefits in offline HCCR and have achieved outstanding results. In this review, we aim to show the different HCCR methods for tackling the complexity and variability of offline HCC writing styles. This paper also reviews different activation functions used in offline HCCR and provides valuable assistance to new researchers in offline Chinese handwriting recognition by providing a succinct study of various methods for recognizing offline HCC.

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Application of Convolutional Neural Network in Handwritten Chinese Character Recognition

Cited by 2 publications

References 5 publications

[Retracted] Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model

[Retracted] Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model

Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods

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