Number Detection System Using CNN

EasyChair Preprint 12500

Abstract

In order to improve the precision and effectiveness of number recognition systems, this research study introduces a novel method of number detection utilizing convolutional neural networks (CNNs). Strong number detection procedures are needed, especially in situations when conventional approaches are inadequate. This is addressed by the suggested system. Customers' overall purchasing experience is improved by the system's enhanced performance in identifying and classifying numbers thanks to the utilization of CNNs. The principal aim of this project is to create a CNN-based number detection system that can be easily incorporated into current retail settings, meeting the needs of customers who need quick checkout procedures and reducing the possibility of disease transmission during busy shopping hours. The CNN model is trained using an extensive collection of numerical images with a range of fonts, styles, and perspectives to guarantee adaptability and broader applicability. The proposed approach is effective, as seen by the fast processing times and high accuracy rates of the experimental results. This streamlines the shopping experience and saves clients important time. The technology is also affordable to adopt, which means that a variety of merchants looking to improve their customers' shopping experiences can use it without having to make a big financial commitment.

Keyphrases: Convolutional Neural Networks, Number detection, computer vision

@booklet{EasyChair:12500,
  author    = {Dhval Nimavath and Pothuri Venkata Hemanth and Ravva Sai Tejash and Reddi Durga Prasad and Dhwaram Avinash Reddi},
  title     = {Number Detection System Using CNN},
  howpublished = {EasyChair Preprint 12500},
  year      = {EasyChair, 2024}}