Moment invariant-based features for Jawi character recognition

Ancient manuscripts written in Malay-Arabic characters, which are known as "Jawi" characters, are mostly found in Malay world. Nowadays, many of the manuscripts have been digitalized. Unlike Roman letters, there is no optical character recognition (OCR) software for Jawi characters. This article proposes a new algorithm for Jawi character recognition based on Hu’s moment as an invariant feature that we call the tree root (TR) algorithm. The TR algorithm allows every Jawi character to have a unique combination of moment. Seven values of the Hu’s moment are calculated from all Jawi characters, which consist of 36 isolated, 27 initial, 27 middle, and 35 end characters; this makes a total of 125 characters. The TR algorithm was then applied to recognize these characters. To assess the TR algorithm, five characters that had been rotated to 90o and 180o and scaled with factors of 0.5 and 2 were used. Overall, the recognition rate of the TR algorithm was 90.4%; 113 out of 125 characters have a unique combination of moment values, while testing on rotated and scaled characters achieved 82.14% recognition rate. The proposed method showed a superior performance compared with the Support Vector Machine and Euclidian Distance as classifier

Classification of Typed Characters Using Backpropagation Neural Network

This thesis concentrates on classification of typed characters using a neural network. Recognition of typed or printed characters using intelligent methods like neural network has found much application in the recent decades. The ability of moment invariants to represent characters independent of position, size and orientation have caused them to be proposed as pattern sensitive features in classification and recognition of these characters. In this research, uppercase English characters is represented by invariant features derived using functions of regular moments, namely Hu invariants. Moments up to the third order have been used for the recognition of these typed characters. A single layer perceptron artificial neural network trained by the backpropagation algorithm is used to classify these characters into their respective categories. Experimental study conducted with three different fonts commonly used in word processing applications shows good classification results. Some suggestions for further work in this area have also been presented

Feature Extraction Methods for Character Recognition

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