1. Visualization of Customized Convolutional Neural Network for Natural Language Recognition.
- Author
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Singh, Tajinder Pal, Gupta, Sheifali, Garg, Meenu, Gupta, Deepali, Alharbi, Abdullah, Alyami, Hashem, Anand, Divya, Ortega-Mansilla, Arturo, and Goyal, Nitin
- Subjects
CONVOLUTIONAL neural networks ,NATURAL languages ,WORD recognition ,VISUALIZATION ,AGE groups - Abstract
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 × 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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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