A. El Yacoubi

An off-line signature verification system using hidden Markov model and cross-validation

The main objective is to present an off-line signature verification system. It is basically divided into three parts. The first demonstrates a pre-processing process, a segmentation process and a feature extraction process, in which the main aim is to obtain the maximum performance quality of the process of verification of random falsifications, in the false acceptance and false rejection concept. The second presents a learning process based on HMM, where the aim is obtaining the best model. That is, one that is capable of representing each writers signature, absorbing yet at the same time discriminating, at most the intrapersonal and interpersonal variation. The third presents a signature verification process that uses the models generated by the learning process without using any prior knowledge of test data, in other words, using an automatic derivation process of the decision thresholds.

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

This paper describes a global model designed to jointly detect and recognize a street name within a delivery line of an handwritten address block image. The model used is based on Hidden Markov Models (HMM). The lines are firstly preprocessed, then segmented and characterized by two types of features. We create a HMM for each street name by simply concatenating the corresponding letter models, elementary HMM learned on a large city name database. A street name may often be surrounded by other information on the left and on the right within the delivery line. These phenomena are roughly modelled by trigrams. The global model is then simply obtained by the concatenation of trigram models with the HMMs corresponding to street names.

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.

Influence of word length on handwriting recognition

Two strategies can be considered in handwriting recognition: phrase or word approaches. In this paper, we demonstrate the superiority of the phrase-based strategy, especially in city name recognition. The performance of an HMM-based off-line system using an analytic approach with explicit segmentation is evaluated on two databases: (i) city names in full, and (ii) city names in single words. A difference in performance is observed, principally caused by the dissimilarity of word lengths between the two databases. After generating other data sets and lexicons, experiments were performed yielding results which lead us to conclude that word length in the data set, as well as in lexicons, significantly influences recognition performance, and also that it is preferable to perform city name recognition based on the phrase approach rather than by word recognition.

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.