Michel Gilloux

An HMM-based approach for off-line unconstrained handwritten word modeling and recognition

Describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentation-symbols, which are both explicitly modeled. The word model is made up of the concatenation of appropriate letter models consisting of elementary HMMs and an HMM-based interpolation technique is used to optimally combine the two feature sets. Two rejection mechanisms are considered depending on whether or not the word image is guaranteed to belong to the lexicon. Experiments carried out on real-life data show that the proposed approach can be successfully used for handwritten word recognition.

An automatic reading system for handwritten numeral amounts on French checks

We present an automatic recognition system applied to handwritten numeral check amounts. This system is based on a segmentation-by-recognition probabilistic model. The application is described from the field amount localization to the hypothesis generation of amounts. An explicit segmentation algorithm determines cut regions on digit links and provides a multiple spatial representation. The best path for the segmentation is determined by the combination of the recognition scores, segmentation weights and the outputs of a probabilistic parser. Training is done by a bootstrapping technique, which significantly improves the performances of the different algorithms. It also allows the use of a reject class at the recognition step. The system was evaluated on 10000 database images to show its robustness.

An MLP-SVM combination architecture for offline handwritten digit recognition

Abstract.This paper presents an original hybrid MLP-SVM method for unconstrained handwritten digits recognition. Specialized Support Vector Machines (SVMs) are introduced to improve significantly the multilayer perceptron (MLP) performance in local areas around the separating surfaces between each pair of digit classes, in the input pattern space. This hybrid architecture is based on the idea that the correct digit class almost systematically belongs to the two maximum MLP outputs and that some pairs of digit classes constitute the majority of MLP substitutions (errors). Specialized local SVMs are introduced to detect the correct class among these two classification hypotheses. The hybrid MLP-SVM recognizer achieves a recognition rate of

A statistical approach for phrase location and recognition within a text line: an application to street name recognition

98.01\%

An hybrid MLP-SVM handwritten digit recognizer

, for real mail zipcode digits recognition task. By introducing a rejection mechanism based on the distances provided by the local SVMs, the error/reject trade-off performance of our recognition system is better than several classifiers reported in recent research.

Learning and Plan Refinement in a Knowledge-Based System for Automatic Speech Recognition

We describe an approach to conjointly locate and recognize a street name within a street line. The system developed is based on a probabilistic framework that naturally integrates various knowledge sources to emit a final decision. At the handwriting signal level, hidden Markov models are extensively used to provide the needed matching scores. Several optimization techniques are employed to speed up the processing time. Experiments carried out on large data sets of street line images, automatically extracted from real French mail envelope images, show very promising results.

Strategies for cursive script recognition using hidden Markov models

This paper presents an original hybrid MLP-SVM method for unconstrained handwritten digits recognition. Specialized support vector machines (SVMs) are introduced to improve significantly the multilayer perceptron (MLP) performances in local areas around the separation surfaces between each pair of digit classes, in the input pattern space. This hybrid architecture is based on the idea that the correct digit class almost systematically belongs to the two maximum MLP outputs and that some pairs of digit classes constitute the majority of MLP substitutions (errors). Specialized local SVMs are introduced to detect the correct class among these two classification hypotheses. The hybrid MLP-SVM recognizer achieves a recognition rate of 98.01%, for real mail zip code digits recognition task, a performance better than several classifiers reported in recent researches.

Strategies for handwritten words recognition using hidden Markov models

This paper shows how a semiautomatic design of a speech recognition system can be done as a planning activity. Recognition performances are used for deciding plan refinement. Inductive learning is performed for setting action preconditions. Experimental results in the recognition of connected letters spoken by 100 speakers are presented.

Research into the new generation of character and mailing address recognition systems at the French post office research center

We describe several approaches for the application of hidden Markov models to the recognition of handwritten words. In all approaches the words are described by strings of symbols. The descriptions differ only with respect to the size of the vocabulary to be recognized. We can define two distinct cases: in the first, the vocabulary is small and constant; in the second, the vocabulary is limited, but dynamic in the sense that it is a varying subset of an open one. We also describe an application of hidden Markov models to the representation of contextual knowledge and propose some strategies for rejecting unreliable word interpretations, in particular, when the word corresponding to the image does not necessarily belong to the lexicon.

Conjoined location and recognition of street names within a postal address delivery line

Several approaches for the application of hidden Markov models to the recognition of handwritten words are described. All approaches share the same description of words through strings of symbols. They differ with respect to the size of the vocabulary which has to be recognized. The authors distinguish between two cases: where the vocabulary is small and constant, and where the vocabulary is limited but dynamic in the sense that it is a varying subset of an open one. The authors also describe an application of hidden Markov models to the representation of contextual knowledge and propose some strategies to reject unreliable word interpretations, in particular when the word corresponding to the image is not guaranteed to belong to the lexicon.<<ETX>>