Benchmarking on offline Handwritten Tamil Character Recognition using convolutional neural networks

Kavitha, B.; Chandrasekaran, Srimathi

doi:10.1016/j.jksuci.2019.06.004

Cited by 68 publications

(38 citation statements)

References 6 publications

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“…Therefore, it is extensively employed in image classifications. The instances of a general CNN are viewed as cited earlier [26] and are illustrated in Fig. 2.…”

Section: Cnn-based Inception V3 Features With Adam Optimizermentioning

confidence: 99%

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

Shankar

Perumal

2020

Complex Intell. Syst.

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COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits. So, the design and implementation of COVID-19 testing kits remain an open research problem. Several findings attained using radio-imaging approaches recommend that the images comprise important data related to coronaviruses. The application of recently developed artificial intelligence (AI) techniques, integrated with radiological imaging, is helpful in the precise diagnosis and classification of the disease. In this view, the current research paper presents a novel fusion model hand-crafted with deep learning features called FM-HCF-DLF model for diagnosis and classification of COVID-19. The proposed FM-HCF-DLF model comprises three major processes, namely Gaussian filtering-based preprocessing, FM for feature extraction and classification. FM model incorporates the fusion of handcrafted features with the help of local binary patterns (LBP) and deep learning (DL) features and it also utilizes convolutional neural network (CNN)-based Inception v3 technique. To further improve the performance of Inception v3 model, the learning rate scheduler using Adam optimizer is applied. At last, multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model was experimentally validated using chest X-ray dataset. The experimental outcomes inferred that the proposed model yielded superior performance with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%.

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“…Therefore, it is extensively employed in image classifications. The instances of a general CNN are viewed as cited earlier [26] and are illustrated in Fig. 2.…”

Section: Cnn-based Inception V3 Features With Adam Optimizermentioning

confidence: 99%

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

Shankar

Perumal

2020

Complex Intell. Syst.

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“…The method is also analyzed and compared with existing techniques for road scene and indoor understanding [ 36 , 37 , 38 ]. CNN has shown remarkable abilities in offline handwritten character recognition of Arabic language [ 39 ]; handwritten Tamil character recognition [ 40 ]; Telugu character recognition [ 41 ], handwritten Urdu text recognition [ 42 , 43 ], handwritten character recognition in Indic scripts [ 44 ] and Chinese handwritten text recognition [ 45 , 46 , 47 ].…”

Section: Related Workmentioning

confidence: 99%

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Ahlawat¹,

Choudhary²,

Nayyar

et al. 2020

Sensors

214

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Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, receptive field, kernel size, padding and dilution for CNN-based handwritten digit recognition. In addition, we aim to evaluate various SGD optimization algorithms in improving the performance of handwritten digit recognition. A network’s recognition accuracy increases by incorporating ensemble architecture. Here, our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy even better than that of ensemble architectures, along with reduced operational complexity and cost. Moreover, we also present an appropriate combination of learning parameters in designing a CNN that leads us to reach a new absolute record in classifying MNIST handwritten digits. We carried out extensive experiments and achieved a recognition accuracy of 99.87% for a MNIST dataset.

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“…The proposed CNN model contained 9 layers including convolutional layers, max pooling layers and fully connected layers. The experiments were performed on HP Labs character dataset containing 82,929 images and achieved 97.7% accuracy with dropout [10]. Pragathi et al described a method for Tamil character recognition using deep learning.…”

Section: Handwriting Recognition Studies In Tamil Languagementioning

confidence: 99%

Review of offline handwritten text recognition in south Indian languages

Anju¹,

Chacko²,

Basheer³

2021

Malaya J. Mat.

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Offline Handwritten character recognition is a popular and challenging area of research under pattern recognition and image processing. In this article, offline handwriting recognition methods performed in south Indian languages including Telugu, Tamil, Kannada and Malayalam are presented. A description about south Indian languages and an overview of general handwriting recognition systems are also presented briefly. Convolutional Neural Networks (CNNs) and classifier combination methods have provided better performance among proposals provided by the researchers.

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Benchmarking on offline Handwritten Tamil Character Recognition using convolutional neural networks

Cited by 68 publications

References 6 publications

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Review of offline handwritten text recognition in south Indian languages

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