Holistic Approach Employing Different Optimizers for the Recognition of District Names Using CNN Model
Main Article Content
A Holistic approach is proposed for the recognition of Handwritten district names of Punjab state which are written in Gurmukhi Script. For the purpose of recognition, a Convolutional Neural Network(CNN) using deep learning is employed. Initially, the dataset of 22000 of images is prepared for all the 22 district names of Punjab state and later a CNNis employed. The proposed CNN architecture having 12 layers is developed and employed using three optimizers: Adam, SGD and RMSprop for the recognition task. Best Average Validation Accuracy achieved for the proposed CNN architecture is 95% and maximum achieved validation accuracy is 99% achieved by employing Adam Optimizer.
How to Cite
Sandhya Sharma, Sheifali Gupta, Neeraj Kumar. (2021). Holistic Approach Employing Different Optimizers for the Recognition of District Names Using CNN Model. Annals of the Romanian Society for Cell Biology, 3294–3306. Retrieved from https://annalsofrscb.ro/index.php/journal/article/view/1803