USING A STATISTICAL LANGUAGE MODEL TO IMPROVE THE PERFORMANCE OF AN HMM-BASED CURSIVE HANDWRITING RECOGNITION SYSTEM

TLDR

The paper presents a system for reading unconstrained handwritten text. The system employs a hidden Markov model enhanced with a statistical language model, avoids line segmentation, and compares language models by perplexity. Experiments demonstrate that incorporating linguistic knowledge beyond the lexicon improves recognition across various language models and large vocabularies.

Abstract

In this paper, a system for the reading of totally unconstrained handwritten text is presented. The kernel of the system is a hidden Markov model (HMM) for handwriting recognition. This HMM is enhanced by a statistical language model. Thus linguistic knowledge beyond the lexicon level is incorporated in the recognition process. Another novel feature of the system is that the HMM is applied in such a way that the difficult problem of segmenting a line of text into individual words is avoided. A number of experiments with various language models and large vocabularies have been conducted. The language models used in the system were also analytically compared based on their perplexity.