Chafic Mokbel

Arabic handwriting recognition using baseline dependant features and hidden Markov modeling

In this paper, we describe a 1D HMM offline handwriting recognition system employing an analytical approach. The system is supported by a set of robust language independent features extracted on binary images. Parameters such as lower and upper baselines are used to derive a subset of baseline dependent features. Thus, word variability due to lower and upper parts of words is better taken into account. In addition, the proposed system learns character models without character pre-segmentation. Experiments that have been conducted on the benchmark IFN/ENIT database of Tunisian handwritten country/village names, show the advantage of the proposed approach and of the baseline-dependant features.

Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition

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.

Combination of HMM-Based Classifiers for the Recognition of Arabic Handwritten Words

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.

Fast communication: Advantages of nonuniform arrays using root-MUSIC

In this paper, we consider the Direction-Of-Arrival (DOA) estimation problem in the Nonuniform Linear Arrays (NLA) case, particularly the arrays with missing sensors. We show that the root-MUSIC algorithm can be directly applied to this case and that it can fully exploit the advantages of using an NLA instead of a Uniform Linear Array (ULA). Using theoretical analysis and simulations, we demonstrate that employing an NLA with the same number of sensors as the ULA, yields better performance. Moreover, reducing the number of sensors while keeping the same array aperture as the ULA slightly modifies the Mean Square Error (MSE). Therefore, thanks to the NLA, it is possible to maintain a good resolution while decreasing the number of sensors. We also show that root-MUSIC presents good performance and is one of the simplest high resolution methods for this type of arrays. Closed-form expressions of the estimator variance and the Cramer-Rao Bound (CRB) are derived in order to support our simulation results. In addition, the analytical expression of the CRB of the NLA to the CRB of the ULA ratio is calculated in order to show the advantages of the NLA.

Solutions for robust recognition over the GSM cellular network

This paper deals with automatic speech recognition robustness for noisy wireless communications. We propose several solutions to improve speech recognition over the cellular network. Two architectures are derived for the recognizer. They are based on hidden Markov models (HMMs) adapted to adverse noise conditions. Then two more specific solutions aiming to alleviate GSM cellular network defects (holes and impulsive noise) are developed. Holes are detected and rejected. Impulsive noises are modeled using mixture density HMMs and a maximum likelihood criterion. These solutions allow a noticeable recognition error reduction. The last one seems to be promising.

Some results from the biosecure talking face evaluation campaign

The BioSecure Network of Excellence has collected a large multi- biometric publicly available database and organized the BioSecure Multimodal Evaluation Campaigns (BMEC) in 20072. This paper reports on the Talking Faces campaign. Open source reference systems were made available to participants and four laboratories submitted executable code to the organizer who performed tests on sequestered data. Several deliberate impostures were tested. It is demonstrated that forgeries are a real threat for such systems. A technological race is ongoing between deliberate impostors and system developers.

Recognition of Arabic Handwritten Words using Contextual Character Models

In this paper we present a system for the off-line recognition of cursive Arabic handwritten words. This system in an enhanced version of our reference system presented in [El-Hajj et al., 05] which is based on Hidden Markov Models (HMMs) and uses a sliding window approach. The enhanced version proposed here uses contextual character models. This approach is motivated by the fact that the set of Arabic characters includes a lot of ascending and descending strokes which overlap with one or two neighboring characters. Additional character models are constructed according to characters in their left or right neighborhood. Our experiments on images of the benchmark IFN/ENIT database of handwritten villages/towns names show that using contextual character models improves recognition. For a lexicon of 306 name classes, accuracy is increased by 0.6% in absolute value which corresponds to a 7.8% reduction in error rate.

Adapting PSN recognition models to the GSM environment by using spectral transformation

In this work, environment adaptation is studied in order to transform PSN speaker independent isolated words HMM to the GSM environment. Linear multiple regression (LMR) transformations associated with groups of HMM densities are used to adapt the densities. Both mean vectors and covariance matrices of the densities are adapted. It has been shown that a small amount of GSM data are sufficient to transform the PSN HMM in order to match the GSM environment and to achieve a performance equivalent to those of an HMM trained with a large amount of GSM data. The number of groups of Gaussian densities seems to have a small influence on the results. However, the minimum number of groups depends on the vocabulary size. Finally, this technique is compared to the Bayesian adaptation and the results show that similar performance can be obtained with both methods.

Talking-face identity verification, audiovisual forgery, and robustness issues

The robustness of a biometric identity verification (IV) system is best evaluated by monitoring its behavior under impostor attacks. Such attacks may include the transformation of one, many, or all of the biometric modalities. In this paper, we present the transformation of both speech and visual appearance of a speaker and evaluate its effects on the IV system. We propose MixTrans, a novel method for voice transformation. MixTrans is a mixture-structured bias voice transformation technique in the cepstral domain, which allows a transformed audio signal to be estimated and reconstructed in the temporal domain. We also propose a face transformation technique that allows a frontal face image of a client speaker to be animated. This technique employs principal warps to deform defined MPEG-4 facial feature points based on determined facial animation parameters (FAPs). The robustness of the IV system is evaluated under these attacks.

Frame-synchronous stochastic matching based on the Kullback-Leibler information

An acoustic mismatch between a given utterance and a model degrades the performance of the speech recognition process. We choose to model speech by hidden Markov models (HMMs) in the cepstrum domain and the mismatch by a parametric function. In order to reduce the mismatch, one has to estimate the parameters of this function. We present a frame synchronous estimation of these parameters. We show that the parameters can be computed recursively. Thanks to such methods, parameters variations can be tracked. We give general equations and study the particular case of an affine transform. Finally, we report recognition experiments carried out over both PSTN and cellular telephone network to show the efficiency of the method in a real context.