Abstract: Continuous Chinese handwriting recognition is the primary bottleneck for Chinese handwritten character input method.Naturally and quickly inputting Chinese text is the fundamental goal to the pattern recognition field even to the artificial intelligence.A novel recognition method was proposed for overlaid Chinese handwriting.It follows a segmentation-recognition integrated framework.Firstly,an over-segmentation algorithm is used to partition the handwriting trajectory.Then a perceptron algorithm is developed to locate the candidate character boundaries.Finally,multiple contexts including character recognition score,geometrical score and linguistic score,are utilized to decode the optimal recognition path.To match different mobile terminals,an appealing compression algorithm was proposed to make the character classifier compact,which reduces the storage consumption both in memory fingerprint and disk storage.The principled method is successfully ported to Android platform,enabling overlaid Chinese handwriting to be input on smart phones and further tested on large overlaid Chinese handwriting samples.Experimental results verify the effectiveness and efficiency of the method.It also works smoothly on smart phone,whose overlapped handwriting input function makes handwriting input remarkably efficient.

Key words: Pattern recognition,Overlaid Chinese handwriting,Stroke classification,Classifier compression,Beam search