Dynamic Text Line Segmentation for Real-Time Recognition of Chinese Handwritten Sentences

Abstract

Real-time recognition of handwritten sentences enables fast text input but the dynamic nature of writing makes reliable text line segmentation difficult. This paper proposes a method for real-time dynamic text line segmentation of online Chinese handwriting. The core of the method is a statistical classifier for modeling the geometric relationship between an ongoing stroke and the previous text lines, to assign the stroke into a previous line or form a new line. The method can deal with delayed strokes and therefore enables robust real-time recognition. We evaluated the segmentation performance on a dataset of online Chinese handwriting by simulating the real-time writing and recognition process. The experimental results demonstrate the effectiveness and robustness of the proposed method.

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“…For Chinese text processing, segmentation (Teahan, Wen, McNab and Witten 2000;Wang and Liu 2011) is an important first step. Unlike English and other western languages, Chinese does not provide inter-word delimiters, so a sentence may have different meanings with different segmentations.…”