Sophisticated topology of hidden Markov models for cursive script recognition

Abstract

Hidden Markov models (HMM) are currently widely used and a successful statistical method for automatic recognition of spoken utterances. This paper describes an adaptation of HMM to automatic recognition of unrestricted handwritten words. Focussed on HMM, we describe many interesting details of a 50,000 vocabulary recognition system for US city names. This system includes feature extraction, classification, estimation of model parameters, and word recognition. The feature extraction module transforms a binary image to a sequence of feature vectors. The classification module consists of a transformation based on linear discriminant analysis and gaussian soft-decision vector quantizers which transform feature vectors into sets of symbols and associated likelihoods. Symbols and likelihoods form the input to both HMM training and recognition. HMM training performed in several succeasiire steps requires only a small amount of gestalt labeled data on the level of characters for initialization. Most of the training material must be only labeled as uppercase ascii word. Step-by-step training is necessary because characters may occur in different styles of writing which is taken into account by a sophisticated model topology. HMM recognition based on the Viterbi algorithm runs on subsets of the whole vocabulary. A subset is selected by the ZIP-code recognition module and a statistically-based estimation of the number of characters in the word to be recognized. HMM recognition uses a mued breadth-first, depth-first search technique. It should be further mentioned that OUT recognition algorithm is segmentation-free, i.e. it works directly on lexicon WO& and not on presegmented characters.

Optimization of Weights in a Multiple Classifier Handwritten Word Recognition System Using a Genetic Algorithm

“…Most of the systems reported in the literature until today only consider constrained recognition problems based on small vocabularies from specific domains, e.g. the recognition of handwritten check amounts [3] or postal addresses [4]. Free handwriting recognition, without domain specific constraints and large vocabularies, was addressed only recently in a few papers [5,6].…”

A lexicon driven approach to handwritten word recognition for real-time applications

“…Segmentation and recognition of cursive script have been adequately described in the literature [4], [9], [10], [11], [12], [13]. The task of segmentation is integrally coupled to the recognition methodology and is analogous to segmenting continuous speech [14].…”

Evaluation of Classical and Novel Ensemble Methods for Handwritten Word Recognition

“…Most of the systems reported in the literature until today consider constrained recognition problems based on vocabularies from specific domains, e.g. the recognition of handwritten check amounts [13] or postal addresses [15]. Free handwriting recognition, without domain specific constraints and large vocabularies, was addressed only recently in a few papers [16,22].…”