COMMAX 2020 UiTM Kampus Kuala Terengganu

Today, handwritten recognition becomes a very crucial area in the field of pattern recognition and image processing. Deep learning was commonly used for handwriting recognition. Recognition of handwritten text is analyzed in offline handwriting. The only information that can be analyzed is a character's binary production against a context. While shifting to digital writing stylus gives more detail, such as pen movement, pressure and writing speed, there is still a need for offline methods when it is unavailable online. Postal address, historical documents, archives, or mass digitization of hand-filled forms are especially needed. Extensive work in this area has led to considerable change from conventional methods to human-competitive performance. The conversion of handwritten image into digital format required more time and often affected by errors. Next, noise has been recognized as one of the major issues that decrease the performance of handwritten recognition system. Therefore, this study will develop handwritten recognition system by using Convolutional Neural Network (CNN) as a classifier. The accuracy achieved from the project is 99.56%. This result will prove that CNN can be the great classifier as it can produce high accuracy rate.