Kannada Handwritten character recognition using SVM, KNN and CNN

Abstract

kannada handwritten character recognition has been an issue of some researchers and analysts Different applications need solution to recognize the cursive nature of handwritten text.

The stated nature of written styles needs to implement. This paper needs, relevant research towards handwritten recognition and how to process how to predict. We  used in the recognition of Kannada handwritten words.

The main aim of proposed work is to identify Kannada handwritten written words in paper or system etc, and to solve recognition problem by using machine learning algorithms. One’s give the kannada word it will predict the correct words. 

System provides a detailed concept on pre-processing, segmentation, classifier used to develop systematic CNN The achieved accuracy is of 96.8% for Kannada handwritten words.

Introduction to Handwritten Character Recognition of Kannada Language Using Convolutional Neural Networks

Recognizing the handwritten Kannada words is the intention of this paper. Instead of using the keyboard and typing Kannada by Nudi in laptop or computer, if graphics tablet is used to write, whatever we give input, that will be automatically printed.

This decreases the burden. One who is unaware of keyboard, even he can write and get the written words in the standard text form. Recognition is done by using Image Processing and Deep Learning. The pen movement on the tablet helps to collect the dataset.

Each data is stored in different folders. Each folder consists of 25 samples of Kannada character. Python language is used to write code for data collection and data prediction.