@article{uninimx653,
            year = {2022},
         journal = {Sensors},
           month = {Abril},
           pages = {2881},
          number = {8},
          volume = {22},
          author = {Tajinder Pal Singh and Sheifali Gupta and Meenu Garg and Deepali Gupta and Abdullah Alharbi and Hashem Alyami and Divya Anand and Arturo Ortega-Mansilla and Nitin Goyal},
           title = {Visualization of Customized Convolutional Neural Network for Natural Language Recognition},
        keywords = {Gurumukhi script; word recognition; convolutional neural network; performance analysis},
             url = {http://repositorio.unini.edu.mx/id/eprint/653/},
        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 {$\times$} 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.}
}