This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.

This article describes the isolated word recognizer presented by the authors to the ICDAR 2009 French handwriting recognition competition. The system is a combination of three isolated word recognizers based on different features and models. A novel n-best combination method is proposed and compared to standard combination methods. New results on the ICDAR 2009 test database are reported.

The problem addressed in this study is the offline recognition of handwritten Arabic city names. The names are assumed to belong to a fixed lexicon of about 1,000 entries. A state-of-the-art classical right-left hidden Markov model (HMM)-based recognizer (reference system) using the sliding window approach is developed. The feature set includes both baseline-independent and baseline-dependent features. The analysis of the errors made by the recognizer shows that the inclination, overlap, and shifted positions of diacritical marks are major sources of errors. In this paper, we propose coping with these problems. Our approach relies on the combination of three homogeneous HMM-based classifiers. All classifiers have the same topology as the reference system and differ only in the orientation of the sliding window. We compare three combination schemes of these classifiers at the decision level. Our reported results on the benchmark IFN/ENIT database of Arabic Tunisian city names give a recognition rate higher than 90 percent accuracy and demonstrate the superiority of the neural network-based combination. Our results also show that the combination of classifiers performs better than a single classifier dealing with slant-corrected images and that the approach is robust for a wide range of orientation angles.

In this paper we present a two-stage system for the off-line recognition of cursive Arabic handwritten words. The proposed method is analytic without segmentation, and is able to cope with handwriting inclination and with shifted positions of diacritical marks. First, the recognition stage relies on 3 classifiers based on hidden Markov modelling (HMM). The second stage depends on the combination of these classifiers. The feature vectors used for recognition are related to pixel density distribution and to local pixel configurations. These vectors are extracted on word binary images by using a sliding window approach with different angles. We have experimented different combination schemes. The neural network-based combined system yields best performance on the IFN- ENIT benchmark data base of handwritten names of Tunisian villages/towns.