Bigram-based post-processing for online handwriting recognition using correctness evaluation

Abstract

An approach to bigram-based linguistic processing for online handwriting text recognition is described. A probability of correctness for each recognition result is derived from a feature set which consists of bigram probabilities and recognition scores. Using the probability of correctness, the number of candidates accepted to the post-processing step and the weight value balancing recognition scores with bigram scores are adaptively controlled. The proposed method is evaluated in experiments using the HANDS-kuchibue online handwritten character database. Results show that the method is effective in reducing candidates, improving accuracy, and saving computational costs.