All makes numerous variations in a character being written by different individuals at different parts of the world.
The complexity of our problem thus lies in the complexity of our language added up to the huge variations in penning down the characters that vary individually.
An accuracy of 81.1% was exhibited in the second phase.
Available online at Direct Procedia Technology 25 (2016) 224 - 231 Global Colloquium in Recent Advancement and Effectual Researches in Engineering, Science and Technology (RAEREST 2016) An Approach towards Malayalam Handwriting Recognition Using Dissimilar Classifiers Meenu Alexa* Smija Dasb Department of Computer Science & Engineering, St.
Adding up to the scene is the similarity in writing styles of different people.
Character recognition has already been successful in foreign languages like English, Japanese, Chinese, Arabic etc.
Therefore Handwriting Recognition is of prime importance.
Handwriting Recognition is an emerging as well as challenging area in the fields of pattern recognition and computer vision.
In the second phase, Malayalam sentences were used.
From the preprocessed image, we were extracted two features: classifier.