TY  - JOUR
UR  - http://doi.org/10.3390/s22082881
Y1  - 2022/04//
VL  - 22
KW  - Gurumukhi script; word recognition; convolutional neural network; performance analysis
SN  - 1424-8220
N2  - 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.
AV  - public
JF  - Sensors
A1  - Singh, Tajinder Pal
A1  - Gupta, Sheifali
A1  - Garg, Meenu
A1  - Gupta, Deepali
A1  - Alharbi, Abdullah
A1  - Alyami, Hashem
A1  - Anand, Divya
A1  - Ortega-Mansilla, Arturo
A1  - Goyal, Nitin
IS  - 8
TI  - Visualization of Customized Convolutional Neural Network for Natural Language Recognition
ID  - uninimx653
ER  -