UNCONSTRAINED HANDWRITING RECOGNITION: LANGUAGE MODELS, PERPLEXITY, AND SYSTEM PERFORMANCE

In this paper we present a number of language models and their behavior in the recognition of unconstrained handwritten English sentences. We use the perplexity to compare the different models and their prediction power, and relate it to the performance of a recognition system under different language models. In the recognition experiments a system with the classical architecture of preprocessing, feature extraction and recognition by means of Hidden Markov Model is used. In the recognition phase the language model constrains the possible next words. Keywords: handwriting recognition, unconstrained English sentence recognition, unigram probability, bigram probability, perplexity.