Fast classification of handwritten on-line Arabic characters

Delaying the analysis launch until the completion of the handwritten word scribing, restricts on-line recognition systems to meet the highly responsiveness demands expected from such applications, and prevents implementing advanced features of input typing such as automatic word completion and real-time automatic spelling. This paper proposes an efficient Arabic handwritten characters recognizer aimed at facilitating real-time handwritten script analysis tasks. The fast classification is enabled by employing an efficient embedding of the feature vectors into a normed wavelet coefficients domain in which the Earth Movers Distance metric is approximated using the Manhattan distance. A sub-linear time character classification is achieved by utilizing metric indexing techniques. Using the results of the top ranked shapes of each predicted character, a list of candidate shapes of Arabic word parts is generated in a filter and refine approach to enable fast yet accurate recognition results in a dictionary-free environment. The system was trained and tested on characters and word parts extracted from the ADAB database, and promising accuracy and performance results were achieved.