Persian digit recognition system in aerial writing based on depth image
Slope Mark Changes,
Hidden Markov Model,
Recognizing handwriting on paper, screen or in the air are some of the challenges in machine vision. Recognizing aerial text has many challenges due to its three-dimensional nature. In this research work, Persian digit recognition is considered in aerial text in which the user writes the digits zero to nine in front of the Kinect sensor in the air and the system is able to detect the above digits using the sensor depth information. In the proposed system, the k-means automatic clustering method is used to separate the hand and fingertip from the background, the proposed linear slope change method is used to extract the feature, and the hidden Markov model (HMM) category is used to identify the feature and figure. The detection accuracy of the proposed system for Persian cultivars with local database and 10-fold cross-validation is 98%. The proposed system was compared with the results of several similar works, these comparisons show that the proposed system works relatively better than the systems under comparison.
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