Title | ||
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Visualization of Customized Convolutional Neural Network for Natural Language Recognition |
Abstract | ||
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For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 x 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset. |
Year | DOI | Venue |
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2022 | 10.3390/s22082881 | SENSORS |
Keywords | DocType | Volume |
Gurumukhi script, word recognition, convolutional neural network, performance analysis | Journal | 22 |
Issue | ISSN | Citations |
8 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
---|---|---|---|
Tajinder Pal Singh | 1 | 0 | 0.34 |
Sheifali Gupta | 2 | 0 | 0.68 |
Meenu Garg | 3 | 0 | 0.34 |
Deepali Gupta | 4 | 0 | 0.34 |
Abdullah Alharbi | 5 | 0 | 0.34 |
Hashem Alyami | 6 | 0 | 0.34 |
Divya Anand | 7 | 2 | 2.20 |
Arturo Ortega-Mansilla | 8 | 0 | 0.68 |
Nitin Goyal | 9 | 0 | 0.34 |