Singh, Tajinder Pal and Gupta, Sheifali and Garg, Meenu and Gupta, Deepali and Alharbi, Abdullah and Alyami, Hashem and Anand, Divya and Ortega-Mansilla, Arturo and Goyal, Nitin UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, divya.anand@uneatlantico.es, arturo.ortega@uneatlantico.es, UNSPECIFIED (2022) Visualization of Customized Convolutional Neural Network for Natural Language Recognition. Sensors, 22 (8). p. 2881. ISSN 1424-8220
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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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Gurumukhi script; word recognition; convolutional neural network; performance analysis |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 06 May 2022 11:58 |
| Last Modified: | 18 Jul 2023 08:25 |
| URI: | http://repositorio.funiber.org/id/eprint/653 |
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