Magdi A. Mohamed

Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques

A lexicon-based, handwritten word recognition system combining segmentation-free and segmentation-based techniques is described. The segmentation-free technique constructs a continuous density hidden Markov model for each lexicon string. The segmentation-based technique uses dynamic programming to match word images and strings. The combination module uses differences in classifier capabilities to achieve significantly better performance.

Fusion of handwritten word classifiers

Methods for fusing multiple handwritten word classifiers are compared on standard data. A novel method based on data-dependent densities in a Choquet fuzzy integral is shown to outperform neural networks, Borda and weighted Borda counts, and Sugeno fuzzy integral.

Advances in fuzzy integration for pattern recognition

Abstract Uncertainty abounds in pattern recognition problems. Therefore, management of uncertainty is an important problem in the development of automated systems for the detection, recognition, and interpretation of objects from their feature measurements. Fuzzy set theory offers numerous methodologies for the modeling and management of uncertainty. One such fuzzy set theoretic technology which has proven quite useful in pattern recognition is the fuzzy integral. The purpose of this paper is to examine new utilizations of the fuzzy integral as a decision making model in the area of object recognition. In particular, we develop generalizations of the fuzzy integral and show that these generalizations can achieve higher recognition rates in an automatic target recognition problem. Also, we demonstrate significant increases in recognition rates using the fuzzy integral to fuse the results of different neural network classifiers in a complex handwritten character recognition domain.

Handwritten word recognition with character and inter-character neural networks

An off-line handwritten word recognition system is described. Images of handwritten words are matched to lexicons of candidate strings. A word image is segmented into primitives. The best match between sequences of unions of primitives and a lexicon string is found using dynamic programming. Neural networks assign match scores between characters and segments. Two particularly unique features are that neural networks assign confidence that pairs of segments are compatible with character confidence assignments and that this confidence is integrated into the dynamic programming. Experimental results are provided on data from the U.S. Postal Service.

Generalized hidden Markov models. I. Theoretical frameworks

In this paper, we present the theoretical framework for the generalization of classical hidden Markov models using fuzzy measures and fuzzy integrals. The main characteristic of the generalization is the relaxation of the usual additivity constraint of probability measures. Fuzzy integrals are defined with respect to fuzzy measures, whose key property is monotonicity with respect to set inclusion. This property is far weaker than the usual additivity property of probability measures. As a result of the new formulation, the statistical independence assumption of the classical hidden Markov models is relaxed. Two attractive properties of this generalization are: the generalized hidden Markov model reduces to the classical hidden Markov model if we used the Choquet fuzzy integral and probability measures; and the establishment of a relation between the generalized hidden Markov model and the classical nonstationary hidden Markov model in which the transitional parameters vary with time.

Neural and fuzzy methods in handwriting recognition

Handwriting recognition requires tools and techniques that recognize complex character patterns and represent imprecise, common-sense knowledge about the general appearance of characters, words and phrases. Neural networks and fuzzy logic are complementary tools for solving such problems. Neural networks, which are highly nonlinear and highly interconnected for processing imprecise information, can finely approximate complicated decision boundaries. Fuzzy set methods can represent degrees of truth or belonging. Fuzzy logic encodes imprecise knowledge and naturally maintains multiple hypotheses that result from the uncertainty and vagueness inherent in real problems. By combining the complementary strengths of neural and fuzzy approaches into a hybrid system, we can attain an increased recognition capability for solving handwriting recognition problems. This article describes the application of neural and fuzzy methods to three problems: recognition of handwritten words; recognition of numeric fields; and location of handwritten street numbers in address images.

Generalized hidden Markov models. II. Application to handwritten word recognition

For part I see ibid. vol.8, no. 1 (2000). This paper presents an application of the generalized hidden Markov models to handwritten word recognition. The system represents a word image as an ordered list of observation vectors by encoding features computed from each column in the given word image. Word models are formed by concatenating the state chains of the constituent character hidden Markov models. The novel work presented includes the preprocessing, feature extraction, and the application of the generalized hidden Markov models to handwritten word recognition. Methods for training the classical and generalized (fuzzy) models are described. Experiments were performed on a standard data set of handwritten word images obtained from the US Post Office mail stream, which contains real-word samples of different styles and qualities.

Comparison of crisp and fuzzy character neural networks in handwritten word recognition

Experiments comparing neural networks trained with crisp and fuzzy desired outputs are described. A handwritten word recognition algorithm using the neural networks for character level confidence assignment was tested on images of words taken from the United States Postal Service mailstream. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level. This empirical result is interpreted as an example of the principle of least commitment. >

Dynamic-programming-based handwritten word recognition using the Choquet fuzzy integral as the match function

The Choquet fuzzy integral is applied to handwritten nword recognition. A handwritten word recognition system is described. The word recognition system assigns a recognition confidence value to each string in a lexicon of candidate strings. The system uses a lexicon-driven approach that integrates segmentation and recognition via dynamic programming matching. The dynamic programming matcher finds a segmentation of the word image for each string in the lexicon. The traditional match score between a segmentation and a string is an average. In this paper, fuzzy integrals are used instead of an average. Experimental results demonstrate the utility of this approach. A surprising result is obtained that indicates a simple choice of fuzzy integral works better than a more complex choice.

Multiple classifier fusion for handwritten word recognition

A method for fusing recognition results from multiple handwritten word recognition algorithms is presented. The fusion algorithm relies on a novel application of the Choquet fuzzy integral. The novel application uses data dependent densities for the fuzzy measure. Three handwritten word recognition algorithms are described. A recognition rate of 88% is achieved on the bd city word test set from standard SUNY CDROM database. This rate is higher than those achieved using Borda counts, weighted counts, and fuzzy integrals with data-independent densities.