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Air-writing refers to virtually writing linguistic characters through handgestures in three-dimensional space with six degrees of freedom. This paperproposes a generic video camera-aided convolutional neural network (CNN) basedair-writing framework. Gestures are performed using a marker of fixed color infront of a generic video camera, followed by color-based segmentation toidentify the marker and track the trajectory of the marker tip. A pre-trainedCNN is then used to classify the gesture. The recognition accuracy is furtherimproved using transfer learning with the newly acquired data. The performanceof the system varies significantly on the illumination condition due tocolor-based segmentation. In a less fluctuating illumination condition, thesystem is able to recognize isolated unistroke numerals of multiple languages.The proposed framework has achieved 97.7%, 95.4% and 93.7% recognition rates inperson independent evaluations on English, Bengali and Devanagari numerals,respectively.

A CNN Based Framework for Unistroke Numeral Recognition in Air-Writing