machine learning models of handwritten character recognition

Jt Savage

ISBN 13: 9798182724272

Published by Savage, 2026

About this Item

machine learning models of handwritten character recognition | Jt Savage | Taschenbuch | Englisch | 2026 | Savage | EAN 9798182724272 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 136479046

Bibliographic Details

• Title: machine learning models of handwritten character recognition
• Publisher: Savage
• Publication Date: 2026
• Language: English
• ISBN 13: 9798182724272
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 8D millimeters 229 millimeters width by 152 millimeters height by 8 millimeters depth
• Weight: 262 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Document Analysis and Recognition (DAR) systems play a critical role in enabling this data exchange. A key component of DAR systems is Optical Character Recognition (OCR), which allows for the recognition of both printed and handwritten text. Handwritten text recognition systems are especially valuable, providing a seamless interface that enhances communication between users and computers, enabling machines to interpret and process handwritten documents. Such systems contribute substantially to bridging the gap between humans and machines. Although considerable research has focused on recognizing characters in Indian scripts, the challenge of efficient data exchange between humans and machines remains unresolved for these scripts. This book addresses these challenges by developing a framework for predicting age, gender, and handedness in handwritten character recognition of the Gurumukhi script, which is used for the Punjabi language, one of India's official languages. Notably, Gurumukhi is the tenth most widely used script globally. It uses feature extraction techniques, performance evaluation metrics in age, gender and handedness, and explores various methods using machine learning models/ neural networks for recognition of indic and non-indic scripts.