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This paper demonstrates the effectiveness of proper and efficient features for classifying online Farsi characters. We use these features to classify the main body of Farsi letters to nine groups. We implemented our method on the main bodies of 4000 isolated letters from "TMU dataset". Correct recognition rates of 99% and 94% were achieved for training and test sets respectively.
On-line handwriting recognition has been a frontier area of research for the last few decades under the purview of pattern recognition. Word processing turns to be a vexing experience even if it is with the assistance of an alphanumeric keyboard in Indian languages. A natural solution for this problem is offered through online character recognition. There is abundant literature on the handwriting...
This paper proposes a novel ldquoair-writingrdquo character recognition system (ACRS) based on optical detection of red light, with which a user can write a character in the air with a red light-emitting device. The trajectories of the light can be captured and detected by a camera during the writing process and then a character reconstruction algorithm is employed to convert them to a 2-D plan (as...
Classification from text/graphics images is an important procedure for automatic digitization of documents. Based on the shapes in the basis image, the asymmetrical filters are developed in this paper. By filtering with the asymmetrical filters, the horizontal, vertical, and inclined edgespsila information are fetched from the input image. With applying a classified algorithm, the input image can...
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