Marisa E. Morita

Unsupervised feature selection using multi-objective genetic algorithms for handwritten word recognition

In this paper a methodology for feature selection in unsupervisedlearning is proposed. It makes use of a multi-objectivegenetic algorithm where the minimization of thenumber of features and a validity index that measures thequality of clusters have been used to guide the search towardsthe more discriminant features and the best numberof clusters. The proposed strategy is evaluated usingtwo synthetic data sets and then it is applied to handwrittenmonth word recognition. Comprehensive experimentsdemonstrate the feasibility and efficiency of the proposedmethodology.

Feature selection for ensembles applied to handwriting recognition

Feature selection for ensembles has shown to be an effective strategy for ensemble creation due to its ability of producing good subsets of features, which make the classifiers of the ensemble disagree on difficult cases. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The underpinning paradigm is the “overproduce and choose”. The algorithm operates in two levels. Firstly, it performs feature selection in order to generate a set of classifiers and then it chooses the best team of classifiers. In order to show its robustness, the method is evaluated in two different contexts:supervised and unsupervised feature selection. In the former, we have considered the problem of handwritten digit recognition and used three different feature sets and multi-layer perceptron neural networks as classifiers. In the latter, we took into account the problem of handwritten month word recognition and used three different feature sets and hidden Markov models as classifiers. Experiments and comparisons with classical methods, such as Bagging and Boosting, demonstrated that the proposed methodology brings compelling improvements when classifiers have to work with very low error rates. Comparisons have been done by considering the recognition rates only.

Feature Selection for Ensembles Using the Multi-Objective Optimization Approach

Summary. Feature selection for ensembles has shown to be an effective strategy for ensemble creation due to its ability of producing good subsets of features, which make the classifiers of the ensemble disagree on difficult cases. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The underpinning paradigm is the “overproduce and choose”. The algorithm operates in two levels. Firstly, it performs feature selection in order to generate a set of classifiers and then it chooses the best team of classifiers. In order to show its robustness, the method is evaluated in two different contexts: supervised and unsupervised feature selection. In the former, we have considered the problem of handwritten digit recognition and used three different feature sets and multi-layer perceptron neural networks as classifiers. In the latter, we took into account the problem of handwritten month word recognition and used three different feature sets and hidden Markov models as classifiers. Experiments and comparisons with classical methods, such as Bagging and Boosting, demonstrated that the proposed methodology brings compelling improvements when classifiers have to work with very low error rates.

Multi-objective genetic algorithms to create ensemble of classifiers

Feature selection for ensembles has shown to be an effective strategy for ensemble creation due to its ability of producing good subsets of features, which make the classifiers of the ensemble disagree on difficult cases. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The algorithm operates in two levels. Firstly, it performs feature selection in order to generate a set of classifiers and then it chooses the best team of classifiers. In order to show its robustness, the method is evaluated in two different contexts: supervised and unsupervised feature selection. In the former, we have considered the problem of handwritten digit recognition while in the latter, we took into account the problem of handwritten month word recognition. Experiments and comparisons with classical methods, such as Bagging and Boosting, demonstrated that the proposed methodology brings compelling improvements when classifiers have to work with very low error rates.

Mathematical morphology and weighted least squares to correct handwriting baseline skew

An approach to correct the baseline handwritten word skew in the image of bank check dates is presented. The main goal of such an approach is to reduce the use of empirical thresholds. The weighted least squares approach is used on the pseudo-convex hull obtained from the mathematical morphology.

Handwritten month word recognition on Brazilian bank cheques

This paper describes an off-line system under development to process unconstrained handwritten dates on Brazilian bank cheques in an omni-writer context. We show here some improvements on our previous work on isolated month word recognition using hidden Markov models (HMM). After preprocessing, a word image is explicitly segmented into characters or pseudo-characters and represented by two feature sequences of equal length, which are combined using HMM. The word models are generated from the concatenation of appropriate character models. In addition to the small date database, we also make use of the legal amount database to increase the frequency of characters in the training and the validation sets. Although this study deals with a limited lexicon, the many similarities among the word classes can affect the performance of the recognition. Experiments show an increase in the average recognition rate from 84% to 91%. Finally, we present our perspectives of future work.

Segmentation and recognition of handwritten dates

Presents an HMM-MLP hybrid system to recognize complex date images written on Brazilian bank cheques. The system first segments implicitly a date image into sub-fields through the recognition process based on an HMM-based approach. Afterwards, the three obligatory date sub-fields are processed by the system (day, month and year). A neural approach has been adopted to work with strings of digits and a Markovian strategy to recognize and verify words. We also introduce the concept of meta-classes of digits, which is used to reduce the lexicon size of the day and year and improve the precision of their segmentation and recognition. Experiments show interesting results on date recognition.

Unsupervised feature selection for ensemble of classifiers

In this paper we discuss a strategy to create ensemble of classifiers based on unsupervised features selection. It takes into account a hierarchical multi-objective genetic algorithm that generates a set of classifiers by performing feature selection and then combines them to provide a set of powerful ensembles. The proposed method is evaluated in the context of handwritten month word recognition, using three different feature sets and hidden Markov models as classifiers. Comprehensive experiments demonstrate the effectiveness of the proposed strategy.

A recognition and verification strategy for handwritten word recognition

In this paper a word recognition and verification scheme based on HMMs is presented. However, the main contribution of the current work lies in the validation of such a strategy. In order to perform this task, we carried out some experiments on word recognition using a legal amount database and then we compared the results reached with other study which makes use of the same database. The experiments demonstrate the efficiency of the strategy we developed for word recognition and verification.

Recognition of handwritten dates on bank checks using an HMM approach

The article presents the first results of our system applied to the automatic recognition of handwritten dates on Brazilian bank checks. Considering the omni-writer context, we detail our recognition module dedicated to processing the month field. This module is based on the combination of holistic and analytical approaches with a fixed lexicon. Both approaches operate with a single explicit segmentation technique to provide a grapheme sequence for the purposed hidden Markov models of each recognizer. We show significant improvements when combining both modules to get a satisfactory recognition rate considering the small database images we work with. Finally, we present various perspectives for future work.