Handwritten alphabet classification in Tamil language using convolution neural network
Handwritten Alphabet Recognition can be defined as the way of detecting characters from images of Handwritten language alphabets. This is one of the important problems that can be solved by Convolution Neural Networks (CNN). Recent developments in CNN have made it possible to expand this problem are...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-01-01
|
| Series: | International Journal of Cognitive Computing in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307424000093 |
| _version_ | 1797262881945812992 |
|---|---|
| author | Jayasree Ravi |
| author_facet | Jayasree Ravi |
| author_sort | Jayasree Ravi |
| collection | DOAJ |
| description | Handwritten Alphabet Recognition can be defined as the way of detecting characters from images of Handwritten language alphabets. This is one of the important problems that can be solved by Convolution Neural Networks (CNN). Recent developments in CNN have made it possible to expand this problem area from English character recognition or Numbers recognition to Regional Languages character recognition, there has not been sufficient studies conducted in the domain of regional languages. This study has attempted to give deep learning approach to Tamil Handwritten Alphabets classification. This article aims to develop 3 models of CNN – THAC-CNN1, THAC-CNN2 and THAC-CNN3 to recognize Tamil Handwritten Alphabets and classify them based on its category. Our proposed models use a combination of benchmark dataset and a customized dataset which totals to over 2800 images of different Tamil alphabets after various data augmentation techniques. The proposed models are compared with a popular image classification pre-trained models - VGG-11 and VGG-16. We use the standard classification metric - accuracy to measure the performance of our proposed models. With our dataset and augmentation techniques, one of our models THAC-CNN1 achieves 97% accuracy on the training dataset and 92.5% accuracy on test dataset as opposed to 72% and 73.5% accuracy on training dataset and test dataset by pre-trained models. |
| first_indexed | 2024-04-25T00:04:10Z |
| format | Article |
| id | doaj.art-d54891bcde4a4f46b4b03921d308625b |
| institution | Directory Open Access Journal |
| issn | 2666-3074 |
| language | English |
| last_indexed | 2024-04-25T00:04:10Z |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj.art-d54891bcde4a4f46b4b03921d308625b2024-03-14T06:16:09ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742024-01-015132139Handwritten alphabet classification in Tamil language using convolution neural networkJayasree Ravi0Department of Computer Science, SVKM's Mithibai College of Arts, Chauhan Institute of Science & Amrutben Jivanlal College of Commerce And Economics (AUTONOMOUS), Vile Parle(W), Mumbai, 400056, Maharashtra, IndiaHandwritten Alphabet Recognition can be defined as the way of detecting characters from images of Handwritten language alphabets. This is one of the important problems that can be solved by Convolution Neural Networks (CNN). Recent developments in CNN have made it possible to expand this problem area from English character recognition or Numbers recognition to Regional Languages character recognition, there has not been sufficient studies conducted in the domain of regional languages. This study has attempted to give deep learning approach to Tamil Handwritten Alphabets classification. This article aims to develop 3 models of CNN – THAC-CNN1, THAC-CNN2 and THAC-CNN3 to recognize Tamil Handwritten Alphabets and classify them based on its category. Our proposed models use a combination of benchmark dataset and a customized dataset which totals to over 2800 images of different Tamil alphabets after various data augmentation techniques. The proposed models are compared with a popular image classification pre-trained models - VGG-11 and VGG-16. We use the standard classification metric - accuracy to measure the performance of our proposed models. With our dataset and augmentation techniques, one of our models THAC-CNN1 achieves 97% accuracy on the training dataset and 92.5% accuracy on test dataset as opposed to 72% and 73.5% accuracy on training dataset and test dataset by pre-trained models.http://www.sciencedirect.com/science/article/pii/S2666307424000093Tamil character recognitionConvolution neural networkDeep learningData augmentation |
| spellingShingle | Jayasree Ravi Handwritten alphabet classification in Tamil language using convolution neural network International Journal of Cognitive Computing in Engineering Tamil character recognition Convolution neural network Deep learning Data augmentation |
| title | Handwritten alphabet classification in Tamil language using convolution neural network |
| title_full | Handwritten alphabet classification in Tamil language using convolution neural network |
| title_fullStr | Handwritten alphabet classification in Tamil language using convolution neural network |
| title_full_unstemmed | Handwritten alphabet classification in Tamil language using convolution neural network |
| title_short | Handwritten alphabet classification in Tamil language using convolution neural network |
| title_sort | handwritten alphabet classification in tamil language using convolution neural network |
| topic | Tamil character recognition Convolution neural network Deep learning Data augmentation |
| url | http://www.sciencedirect.com/science/article/pii/S2666307424000093 |
| work_keys_str_mv | AT jayasreeravi handwrittenalphabetclassificationintamillanguageusingconvolutionneuralnetwork |