%D 2022
%V 22
%L uninimx653
%R doi:10.3390/s22082881
%X 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.
%P 2881
%K Gurumukhi script; word recognition; convolutional neural network; performance analysis
%J Sensors
%A Tajinder Pal Singh
%A Sheifali Gupta
%A Meenu Garg
%A Deepali Gupta
%A Abdullah Alharbi
%A Hashem Alyami
%A Divya Anand
%A Arturo Ortega-Mansilla
%A Nitin Goyal
%T Visualization of Customized Convolutional Neural Network for Natural Language Recognition
%N 8