Evaluation of Geometric Context Models for Handwritten Numeral String Recognition

Character string recognition based on over segmentation by integrating character classifier and context models has been demonstrated successful. Geometric context models characterizing the candidate character likeliness and between character relationship have shown benefits in several scripts but have not been evaluated in numeral string recognition. Compared with Chinese scripts mixed with alphanumeric and punctuation marks, numeral strings are less variant in character outline and between-character relationship. This study, via evaluating geometric context models used in Chinese handwritten text recognition, shows that geometric context is beneficial to handwritten numeral string recognition as well. Particularly, we propose an improved binary geometric model that combines single-character and between-character features such that the model functions like a bi-character classifier. Combining this binary geometric model with unary geometric model and character classifier, we obtain significant improvement of numeral string recognition performance on the NIST SD-19.