Artificial learning based on convolution neural networks | Application to the recognition of handwritten digits on bank cheques

Bienvenue Bukasa Muepu (u. a.)

ISBN 10: 6208578213 / ISBN 13: 9786208578213

Published by Our Knowledge Publishing, 2025

About this Item

Artificial learning based on convolution neural networks | Application to the recognition of handwritten digits on bank cheques | Bienvenue Bukasa Muepu (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | Our Knowledge Publishing | EAN 9786208578213 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Seller Inventory # 131039064

Bibliographic Details

• Title: Artificial learning based on convolution neural networks | Application to the recognition of handwritten digits on bank cheques
• Publisher: Our Knowledge Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 6208578213
• ISBN 13: 9786208578213
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 113 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Numerical recognition of bank cheques presents a major challenge and plays an important role in today's world, as machines must be able to learn like humans and solve complex problems such as recognizing the digits of bank cheques. Despite attempts to make machines learn like humans, no machine has yet been able to recognize 100% of handwritten digits. Despite attempts to make machines capable of learning like humans, no machine is yet 100% capable of recognizing handwritten digits. It aims to build a prediction model called a classifier that will facilitate this recognition from data in the MNIST database, with a view to possibly helping banks to speed up the processing of banking transactions by cheque.The approach proposed here essentially consists of two steps: feature extraction and classification of image pixels using a convolutional neural network, one of the deep learning algorithms with a proven track record in image processing.