An Analysis of Hand Written Strings of Devanagari Characters Using Artificial Neural Networks

Improving Efficiency and Accuracy in Handwritten Character Identification

Abstract

The identification of handwritten numbers or characters has applications in the area of pattern recognition, text preparation and interpretation. However, the identification of handwritten numbers or characters is a difficult activity for the machine, as the writing styles differ from individual to person. The identification of handwritten numbers or characters is typically a standard concern for Pattern Recognition and Artificial Intelligence. The suggested scheme shows that it is used for the identification of handwritten English numerals identified by the said feature set. This approach to the identification of handwritten English numerals by the MPL classifier can also be expanded to include handwritten English alphabet characters. The images are processed by human operators in many of these real world applications. Automation, however, will increase efficiency and reduce costs. To do this, an automated system's efficiency has to compare favorably with human performance.