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Air-writing recognition system for Persian numbers with a novel classifier

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Abstract

Air-writing through hand or fingertip is a functional and attractive mechanism. Since there is usually no pen-up and pen-down in air-writing, trajectory of numbers and words in the air-writing will be a connected series of characters (ligature Stroke). Identification of legitimate characters such as digits or letters inside a ligature Stroke is one of the most important challenges faced in this area. By solving these challenges, there will be more uses in future. In this work, the color and depth images of the Kinect sensor are used to identify the user’s air-writing, which includes the digits and numbers of the Persian language. To extract a feature vector from the trajectory, we propose a simple but very effective method, called slope variations detection, which is robust to variations of size, translation, and rotation of the trajectory. Also, a novel analytical classifier is proposed to map a vector to a character. This classifier has higher speed and accuracy than traditional classifiers, such as SVM, HMM, and K Nearest Neighbors. Experimental results show that the average recognition rate for digits and numbers of Persian language is 98% which is quite acceptable for a practical system.

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Authors and Affiliations

  1. Department of Engineering, University of Zanjan, Zanjan, Iran

    Shahram Mohammadi & Reza Maleki

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  1. Shahram Mohammadi
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  2. Reza Maleki
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Correspondence to Shahram Mohammadi.

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Mohammadi, S., Maleki, R. Air-writing recognition system for Persian numbers with a novel classifier. Vis Comput 36, 1001–1015 (2020). https://doi.org/10.1007/s00371-019-01717-3

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