Evaluation of deep learning models for Urdu handwritten characters recognition

Jiang, Weiwei

doi:10.1088/1742-6596/1544/1/012016

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…In this article, we mainly use the CNN model, which has been proven effective for handwritten characters recognition [15][16][17]. Therefore, it is meaningful to briefly explain its development and basic principles before launching.…”

Section: Related Workmentioning

confidence: 99%

Ancient character recognition with deep learning techniques

Wang

2023

ACE

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Many deep learning models have achieved remarkable results in many areas, such as image classification and image generation. At the same time, with the increasing attention given to the digitization of ancient manuscripts, ancient character recognition has become one of the most fascinating research areas. In this article, we try some CNNs such as ResNet, VGG, AlexNet or simply CNN on the dataset named Oracle-MNIST, an open ancient character dataset. In addition, to improve the accuracy of the models, ensemble learning is also adopted. Compared with the accuracy, the number of model parameters and running time, it was found that one simple CNN model trained as a snapshot performed best, and the recognition accuracy rate reached 97.009%.

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Section: Related Workmentioning

confidence: 99%

Ancient character recognition with deep learning techniques

Wang

2023

ACE

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“…The dancing robots' performance style must match the music style. In recent years, researchers have applied neural networks (NNs) to audio signal processing (ASP) (Jiang, 2020 ; Er et al, 2021 ). Over time, many variants of RNN have been developed and applied to ASP.…”

Section: Design Of Music Style Recognition Model and Construction Of ...mentioning

confidence: 99%

Recognition of musical beat and style and applications in interactive humanoid robot

Chu

2022

Front. Neurorobot.

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The musical beat and style recognition have high application value in music information retrieval. However, the traditional methods mostly use a convolutional neural network (CNN) as the backbone and have poor performance. Accordingly, the present work chooses a recurrent neural network (RNN) in deep learning (DL) to identify musical beats and styles. The proposed model is applied to an interactive humanoid robot. First, DL-based musical beat and style recognition technologies are studied. On this basis, a note beat recognition method combining attention mechanism (AM) and independent RNN (IndRNN) [AM-IndRNN] is proposed. The AM-IndRNN can effectively avoid gradient vanishing and gradient exploding. Second, the audio music files are divided into multiple styles using the music signal's temporal features. A human dancing robot using a multimodal drive is constructed. Finally, the proposed method is tested. The results show that the proposed AM-IndRNN outperforms multiple parallel long short-term memory (LSTM) models and IndRNN in recognition accuracy (88.9%) and loss rate (0.0748). Therefore, the AM-optimized LSTM model has gained a higher recognition accuracy. The research results provide specific ideas for applying DL technology in musical beat and style recognition.

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