Learning-Based Candidate Segmentation Scoring for Real-Time Recognition of Online Overlaid Chinese Handwriting

In overlaid handwriting, multiple characters are written sequentially in the same area. This needs special consideration for segmenting the stroke sequence into characters. We propose a learning-based model for scoring the candidate stroke cuts and segments for online overlaid Chinese handwriting recognition. Based on stroke cut classification using support vector machine (SVM), strokes are grouped into segments, and consecutive segments are concatenated into candidate characters. The likeliness of candidate characters (unary geometry) and the compatibility between adjacent characters (binary geometry) are measured by combining the stroke cut score and the between-segment geometric score, and are integrated with the character classification score and linguistic context for character string recognition. Experiments on a large database of online Chinese handwriting demonstrate the effectiveness of the proposed method.