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Source publication
This paper is proposing the method for Thai handwritten character recognition. The methods are Robust C-Prototype and Back-Propagation Neural Network. The objective of experimental is recognition on Thai handwritten character. This is the result of both methods to be appearing accuracy more than 85%.
Similar publications
This paper is proposing the method for Thai handwritten character recognition. The methods are Robust C-Prototype and Back-Propagation
Neural Network. The objective of experimental is recognition on Thai handwritten character. This is the result of both methods
to be appearing accuracy more than 85%.
Citations
... [1] When we convert it into a grey scale (or "intensity") image it depends on the sensitivity response curve of detector to light as a function of wavelength. [6,7] The equation is: ...
Palm leaf manuscripts were one of the earliest forms of written media and were used in Southeast Asia to store early written knowledge about subjects such as medicine, Buddhist doctrine and astrology. Therefore, historical handwritten palm leaf manuscripts are important for people who like to learn about historical documents, because we can learn more experience from them. This paper presents an image segmentation of historical handwriting from palm leaf manuscripts. The process is composed of three steps: 1) background elimination to separate text and background by Otsu’s algorithm 2) line segmentation and 3) character segmentation by histogram of image. The end result is the character’s image. The results from this research may be applied to optical character recognition (OCR) in the future
Data reduction is an important step in machine learning and big data analysis. The handwritten recognition is a problem that uses a lot of data to get the good results. Thus, the attribute reduction can be applied to improve the accuracy of classification and reduces the learning time. In this paper, the attribute reduction techniques are studies. These techniques are applied to the Thai handwritten recognition problems. Support vector machines (SVMs) are used to verify the results of 4 attribute reduction techniques, i.e., principle component analysis (PCA), local discriminant analysis (LDA), locality preserving projection (LPP), and neighborhood preserving embedding (NPE). All of these 4 techniques will transform the original attributes to a new space with the different methods. The results show that LDA is a suitable data reduction technique for classifying the handwritten character with SVM. Only 10 % of features can give the accuracy about 47.68 % for 89 classes of the characters. This technique may give a better result when the suitable feature extraction techniques are applied.
