KAZAKH HANDWRITING RECOGNITION

Abstract

Recognition of handwritten text is one aspect of object recognition and known as handwriting detection cause of a computer’s potential to recognize and com-prehend readable handwriting from resources including paper files, touch smart devices, images, etc. Data is categorized into a number of classes or groups using pattern recognition. The best technique for overcoming handwriting identification challenges is convolutional neural networks (CNNs), which are excellent at comprehending the pattern of handwritten characters and words in a way that makes it easy to automatically extract distinguishing features. This method will be used to distinguish between texts in different forms. Numerous new types of handwritten characters have emerged as a result of the evolution of handwriting, including numbers, cursive writing, signs, and scripts in the Kazakh language that we’re using to detect.