Verification of Unconstrained Handwritten Words at Character Level

Abstract

In this paper we present a verification module that has as input the output provided by a word recognizer which is based on the segmentation-recognition paradigm. The word recognizer models words as the concatenation of character hidden Markov models (HMMs) and it provides at the output a list with the Top N best word hypotheses, including their likelihoods and the segmentation points of the words into sub words, which ideally should be characters. The verification module uses the segmentation points provided by the word recognizer for each word hypothesis to extract different features from each sub word. A classifier based on a multilayer perceptron neural network assigns a character class (A-Z) and estimates the a posteriori probability to each sub word that make up a word. Further, both the character class and the a posteriori probabilities are combined with the original output of the word recognizer to re-rank the word hypothesis into the Top N list. Experimental results show that the verification module improves the Top 1 recognition rate in 3.9% for an 85,092-word recognition task.