Bangla-Meitei Mayek scripts handwritten character recognition using Convolutional Neural Network

Hazra, Abhishek; Choudhary, Prakash; Inunganbi, Sanasam Chanu; Adhikari, Mainak

doi:10.1007/s10489-020-01901-2

Cited by 25 publications

(9 citation statements)

References 43 publications

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“…In this study, we chose to create a custom neural network by designing its structure and training the whole network using our own data. The reason is some reports have shown this "built your own network from scratch" can achieve better performance than transfer learning [26,27].…”

Section: Improvement Ii: Stochastic Pooling Neural Networkmentioning

confidence: 99%

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Wang

Zhang

Cheng

et al. 2021

Computational and Mathematical Methods in Medicine

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Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

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Section: Improvement Ii: Stochastic Pooling Neural Networkmentioning

confidence: 99%

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Wang

Zhang

Cheng

et al. 2021

Computational and Mathematical Methods in Medicine

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“…The model has won the ICFHR2018 Competition on Automated Text Recognition on a READ dataset. The authors in [20] proposed a new architecture of a deep CNN with high recognition performance that is capable of learning deep features for visualization. According to the evaluation on the ICDAR-2013 offline HCCR competition dataset, the model has a relative 0.83% error reduction while having 49% fewer parameters and the same computational cost compared to the current state-of-the-art single-network method trained only on handwritten data.…”

Section: Related Workmentioning

confidence: 99%

Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model

2023

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Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu_C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A, a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiNet-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.

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“…Next, as a proxy for wind speed and cloud movement, we examined the degree to which radar echo maps influenced flood height in the target location at intervals of 30 min (30 min, 60 min, 90 min, 120 min, and 150 min). We employed the well-known early stopping procedure, which has proven highly effective in preventing the problem of overfitting during model training in a range of applications [40][41][42]. The judgment benchmark was 20 epochs, and the Adam optimizer was used with an initial learning rate of 0.001.…”

Section: Data Sets and Experiments Parametersmentioning

confidence: 99%

Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps

Chen

Chang

Chow

et al. 2022

Water

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Recent climate change has brought extremely heavy rains and widescale flooding to many areas around the globe. However, previous flood prediction methods usually require a lot of computation to obtain the prediction results and impose a heavy burden on the unit cost of the prediction. This paper proposes the use of a deep learning model (DLM) to overcome these problems. We alleviated the high computational overhead of this approach by developing a novel framework for the construction of lightweight DLMs. The proposed scheme involves training a convolutional neural network (CNN) by using a radar echo map in conjunction with historical flood records at target sites and using Grad-Cam to extract key grid cells from these maps (representing regions with the greatest impact on flooding) for use as inputs in another DLM. Finally, we used real radar echo maps of five locations and the flood heights record to verify the validity of the method proposed in this paper. The experimental results show that our proposed lightweight model can achieve similar or even better prediction accuracy at all locations with only about 5~15% of the operation time and about 30~35% of the memory space of the CNN.

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Bangla-Meitei Mayek scripts handwritten character recognition using Convolutional Neural Network

Cited by 25 publications

References 43 publications

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model

Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps

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