Segmentation and Identification of Bilingual Offline Handwritten Scripts (Devanagari and Roman)

doi:10.35940/ijrte.b1178.0782s619

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Additionally, [5] conducted layer optimization and architectural hyperparameter tuning, achieving a remarkable accuracy rate of 95% on the Cifar-10 dataset. Meanwhile, references [6] (for Cifar-10) and [7] (for MNIST) harnessed GA [8] to comprehensively optimize all facets of CNN hyperparameters, including layers, architecture, and global parameters.…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

Optimizing Convolutional Neural Network (CNN) Models for Enhanced English-Devanagari Handwritten Character Recognition: A Hybrid Evolutionary Approach with Variable Length Genetic Algorithm

Taneja,

Tyagi,

Jain

et al. 2023

IJRASET

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Convolutional Neural Networks (CNNs) have gained widespread recognition in the field of computer vision, specifically for handwritten digit recognition. Despite their remarkable accuracy, CNNs entail significant computational training demands and are susceptible to local optima, necessitating innovative optimization strategies. This research introduces a novel approach to hyperparameter tuning for CNN models tailored to the recognition of English-Devanagari handwritten digits. This method combines a Hybrid Evolutionary Algorithm (HEA) with a Variable Length Genetic Algorithm (VLGA) and leverages a comprehensive dataset encompassing both English and Devanagari handwritten digits. The strategy extends the conventional paradigm by integrating a variable-length GA, facilitating systematic and adaptive tuning of critical CNN hyperparameters, including optimizer selection, learning rate, global hyperparameters, kernel size, filter count, activation functions, layer count, and pooling mechanisms. Extensive experimentation across benchmark datasets underscores the superior performance of the proposed approach when compared to traditional optimization methods. Seven key hyperparameters are the focal point of optimization efforts: learning rate, optimizer, kernel size, filter count, activation function, layer count, and pooling strategy. The results highlight the significant performance boost achieved by CNNs assisted by genetic algorithms, underscoring the effectiveness of evolutionary approaches in CNN training. Notably, experiments reveal that a population size of 27 yields optimal fitness values and average fitness scores. In the culmination of this research, the HEA-VLGA model achieves an impressive accuracy rate of approximately 99.38%. These findings unequivocally affirm the efficacy of incorporating evolutionary techniques as a potent avenue for enhancing CNN training processes.

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Section: B Convolutional Neural Networkmentioning

confidence: 99%

Optimizing Convolutional Neural Network (CNN) Models for Enhanced English-Devanagari Handwritten Character Recognition: A Hybrid Evolutionary Approach with Variable Length Genetic Algorithm

Taneja,

Tyagi,

Jain

et al. 2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…They report that the proposed model performs better than manually hyperparameter optimization. Layer optimization and hyperparameter architecture were carried out by [15] with an accuracy test reaching 95% on the Cifar-10 dataset. Meanwhile, in [16] for Cifar-10 and in [17] for MNIST used GA [18] to optimize all parts of the CNN hyperparameter (layer, architecture, global).…”

Section: Introductionmentioning

confidence: 99%

Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition

Munsarif

Noersasongko²,

Andono³

et al. 2023

Int. J. Adv. Intell. Informatics

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Convolutional Neural Networks (CNNs) perform well compared to other deep learning models in image recognition, especially in handwritten alphabetic numeral datasets. CNN's challenging task is to find an architecture with the right hyperparameters. Usually, this activity is done by trial and error. A genetic algorithm (GA) has been widely used for automatic hyperparameter optimization. However, the original GA with fixed chromosome length allows for suboptimal solution results because CNN has a variable number of hyperparameters depending on the depth of the model. Previous work proposed variable chromosome lengths to overcome the drawbacks of native GA. This paper proposes a variable length GA by adding global hyperparameters, namely optimizer and learning speed, to systematically and automatically tune CNN hyperparameters to improve performance. We optimize seven hyperparameters, such as the learning rate. Optimizer, kernel, filter, activation function, number of layers and pooling. The experimental results show that a population of 25 produces the best fitness value and average fitness. In addition, the comparison results show that the proposed model is superior to the basic model based on accuracy. The experimental results show that the proposed model is about 99.18% higher than the baseline model.

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Segmentation and Identification of Bilingual Offline Handwritten Scripts (Devanagari and Roman)

Cited by 2 publications

References 8 publications

Optimizing Convolutional Neural Network (CNN) Models for Enhanced English-Devanagari Handwritten Character Recognition: A Hybrid Evolutionary Approach with Variable Length Genetic Algorithm

Optimizing Convolutional Neural Network (CNN) Models for Enhanced English-Devanagari Handwritten Character Recognition: A Hybrid Evolutionary Approach with Variable Length Genetic Algorithm

Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition

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