M. Cheriet - Academia.edu (original) (raw)

Papers by M. Cheriet

First International Symposium on Control, Communications and Signal Processing, 2004., 2004

In this paper, we present a new handwritten character recognition algorithm. The proposed algorit... more In this paper, we present a new handwritten character recognition algorithm. The proposed algorithm is based on three main steps. In the first one the original characters are segmented using separable kernel compact support (SKCS) method. In the second step a preprocessing phases: skeleton, separation, resizing, and a polyline approximation processes are then applied to the SKCS segmented characters. In

Pattern Recognition, 2007

Tuning SVM hyperparameters is an important step in achieving a high-performance learning machine.... more Tuning SVM hyperparameters is an important step in achieving a high-performance learning machine. It is usually done by minimizing an estimate of generalization error based on the bounds of the leave-one-out (LOO) such as radius-margin bound and on the performance measures such as Generalized Approximate Cross Validation (GACV), empirical error, etc. These usual automatic methods used to tune the hyperparameters require an inversion of the Gram-Schmidt matrix or a resolution of an extra-quadratic programming problem. In the case of a large dataset these methods require the addition of huge amounts of memory and a long CPU time to the already significant resources used in SVM training. In this paper, we propose a fast method based on an approximation of the gradient of the empirical error, along with incremental learning, which reduces the resources required both in terms of processing time and of storage space. We tested our method on several benchmarks, which produced promising results confirming our approach. Furthermore, it is worth noting that the gain time increases when the dataset is large.

2009 International Joint Conference on Neural Networks, 2009

Help-training for semi-supervised learning was proposed in our previous work in order to reinforc... more Help-training for semi-supervised learning was proposed in our previous work in order to reinforce self-training strategy by using a generative classifier along with the main discriminative classifier. This paper extends the Help-training method to least squares support vector machine (LS-SVM) where labeled and unlabeled data are used for training. Experimental results on both artificial and real problems show its usefulness

Pattern Recognition Letters, 1993

This paper gives an assessment of the current state of the art in handwriting recognition. It sum... more This paper gives an assessment of the current state of the art in handwriting recognition. It summarizes the lessons learned, the difficulties involved, and the challenges ahead. Based on a review of the recent achievements in off-line computer recognition of totally unconstrained ...

IEEE Transactions on Medical Imaging, 2000

A 2D/3D nonrigid registration method is proposed that brings a 3D centerline model of the coronar... more A 2D/3D nonrigid registration method is proposed that brings a 3D centerline model of the coronary arteries into correspondence with bi-plane fluoroscopic angiograms. The registered model is overlaid on top of interventional angiograms to provide surgical assistance during image-guided chronic total occlusion procedures, thereby reducing the uncertainty inherent in 2D interventional images. The proposed methodology is divided into two parts: global structural alignment and local nonrigid registration. In both cases, vessel centerlines are automatically extracted from the 2D fluoroscopic images, and serve as the basis for the alignment and registration algorithms. In the first part, an energy minimization method is used to estimate a global affine transformation that aligns the centerline with the angiograms. The performance of nine general purpose optimizers has been assessed for this problem, and detailed results are presented. In the second part, a fully nonrigid registration method is proposed and used to compensate for any local shape discrepancy. This method is based on a variational framework, and uses a simultaneous matching and reconstruction process to compute a nonrigid registration. With a typical run time of less than 3 s, the algorithms are fast enough for interactive applications. Experiments on five different subjects are presented and show promising results.

Computer Vision and Image Understanding, 2013

Almost all binarization methods have a few parameters that require setting. However, they do not ... more Almost all binarization methods have a few parameters that require setting. However, they do not usually achieve their upper-bound performance unless the parameters are individually set and optimized for each input document image. In this work, a learning framework for the optimization of the binarization methods is introduced, which is designed to determine the optimal parameter values for a document image. The framework, which works with any binarization method, has a standard structure, and performs three main steps: (i) extracts features, (ii) estimates optimal parameters, and (iii) learns the relationship between features and optimal parameters. First, an approach is proposed to generate numerical feature vectors from 2D data. The statistics of various maps are extracted and then combined into a final feature vector, in a nonlinear way. The optimal behavior is learned using support vector regression (SVR). Although the framework works with any binarization method, two methods are considered as typical examples in this work: the grid-based Sauvola method, and Lu's method, which placed first in the DIB-CO'09 contest. The experiments are performed on the DIBCO'09 and H-DIBCO'10 datasets, and combinations of these datasets with promising results.

Proceedings of the Fourth International Conference on Document Analysis and Recognition, 1997

This paper presents a model for reading cursive script which has an architecture inspired by a re... more This paper presents a model for reading cursive script which has an architecture inspired by a reading model and which is based on perceptual concepts. We limit the scope of our study to the o -line recognition of isolated cursive words. First of all, we justify why we chose McClelland & Rumelhart's reading model as the inspiration for our system. A brief resum e of the method's behavior is presented, and the main originalities of our model are underlined. After this, we focus on the new updates added to the original system: a new baseline extraction module, a new feature extraction module, and a new generation, validation and hypothesis insertion process. After implementation of our method, new results have been obtained on real images from a training set of 184 images, and a testing set of 100 images, and are discussed. We are concentrating now on validating the model using a larger database.

Preprocessing and postprocessing steps significantly improve the performance of binarization meth... more Preprocessing and postprocessing steps significantly improve the performance of binarization methods, especially in the case of severely-degraded historical documents. In this paper, an unsupervised postprocessing method is introduced based on the phase-preserved denoised image and also phase congruency features extracted from the input image. The core of the method consists of two robust mask images that can be used to cross out false positive pixels on the output of the binarization method. First, a mask with a high recall value is obtained from the denoised image using morphological operations. In parallel, a second mask is obtained based on phase congruency features. Then, a median filter is used to remove noise on these two masks, which then are used to correct the output of any binarization method. This approach was tested along with several state-ofthe-art binarization methods on the DIBCO'09, H-DIBCO'10, DIBCO'11 and H-DIBCO'12 datasets with promising and robust results. Furthermore, the high performance of the proposed masks shows their potential use as unsupervised semi-ground truth generator for learning-based binarization methods.

The Support Vector Machine (SVM) is an interesting classifier with excellent power of generalizat... more The Support Vector Machine (SVM) is an interesting classifier with excellent power of generalization. In this paper, we consider applying the SVM to semi-supervised learning. We propose using an additional criterion with the standard formulation of the semi-supervised SVM (S 3 VM) to reinforce classifier regularization. Since, we deal with nonconvex and combinatorial problem, we use a genetic algorithm to

Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2, 2007

This article describes an off-line handwritten Arabic words recognition system. Both explicit gra... more This article describes an off-line handwritten Arabic words recognition system. Both explicit graphem segmentation and feature extraction are originally designed for Latin cursive handwriting. The recognizer itself is a Hybrid HMM/NN. We introduce a new shape-based alphabet for handwriting Arabic recognition which is intended to benefit from ofsome specificities of Arabic writing.