Mohamed Cheriet

A recursive thresholding technique for image segmentation

In this correspondence, we present a general recursive approach for image segmentation by extending Otsus (1978) method. The new approach has been implemented in the scope of document images, specifically real-life bank checks. This approach segments the brightest homogeneous object from a given image at each recursion, leaving only the darkest homogeneous object after the last recursion. The major steps of the new technique and the experimental results that illustrate the importance and the usefulness of the new approach for the specified class of document images of bank checks will be presented.

A multi-scale framework for adaptive binarization of degraded document images

In this work, a multi-scale binarization framework is introduced, which can be used along with any adaptive threshold-based binarization method. This framework is able to improve the binarization results and to restore weak connections and strokes, especially in the case of degraded historical documents. This is achieved thanks to localized nature of the framework on the spatial domain. The framework requires several binarizations on different scales, which is addressed by introduction of fast grid-based models. This enables us to explore high scales which are usually unreachable to the traditional approaches. In order to expand our set of adaptive methods, an adaptive modification of Otsus method, called AdOtsu, is introduced. In addition, in order to restore document images suffering from bleed-through degradation, we combine the framework with recursive adaptive methods. The framework shows promising performance in subjective and objective evaluations performed on available datasets.

Databases for recognition of handwritten Arabic cheques

This paper describes an e0ort towards the development of Arabic cheque databases for research in the recognition of hand-written Arabic cheques. Databases of real-life Arabic legal amounts, Arabic sub-words, courtesy amounts, Indian digits, and Arabic cheques are described. This paper highlights some characteristics of the Arabic language and presents the various steps that have been completed to build these databases including segmentation, binarization and data tagging. It also describes a solid validation procedure including grammars and algorithms used to verify the correctness of the tagging process. Detailed descriptions of the database organization and class distribution are included. These databases aim to facilitate experimental comparisons between various recognition method s, andwill be provid edto all interestedresearchers upon request to

Building a new generation of handwriting recognition systems

Abstract 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 handwritten characters, and extensive research, the authors attempt to identify new frontiers for research which may lead to further breakthroughs in this field. They will present some evidences and novel ideas on ways of stretching the limits of handwriting recognition systems aiming at outperforming human beings.

Model selection for the LS-SVM. Application to handwriting recognition

The support vector machine (SVM) is a powerful classifier which has been used successfully in many pattern recognition problems. It has also been shown to perform well in the handwriting recognition field. The least squares SVM (LS-SVM), like the SVM, is based on the margin-maximization principle performing structural risk minimization. However, it is easier to train than the SVM, as it requires only the solution to a convex linear problem, and not a quadratic problem as in the SVM. In this paper, we propose to conduct model selection for the LS-SVM using an empirical error criterion. Experiments on handwritten character recognition show the usefulness of this classifier and demonstrate that model selection improves the generalization performance of the LS-SVM.

Stroke-model-based character extraction from gray-level document images

Global gray-level thresholding techniques such as Otsus method, and local gray-level thresholding techniques such as edge-based segmentation or the adaptive thresholding method are powerful in extracting character objects from simple or slowly varying backgrounds. However, they are found to be insufficient when the backgrounds include sharply varying contours or fonts in different sizes. A stroke-model is proposed to depict the local features of character objects as double-edges in a predefined size. This model enables us to detect thin connected components selectively, while ignoring relatively large backgrounds that appear complex. Meanwhile, since the stroke width restriction is fully factored in, the proposed technique can be used to extract characters in predefined font sizes. To process large volumes of documents efficiently, a hybrid method is proposed for character extraction from various backgrounds. Using the measurement of class separability to differentiate images with simple backgrounds from those with complex backgrounds, the hybrid method can process documents with different backgrounds by applying the appropriate methods. Experiments on extracting handwriting from a check image, as well as machine-printed characters from scene images demonstrate the effectiveness of the proposed model.

Automatic reading of cursive scripts using a reading model and perceptual concepts

Abstract. This paper presents a model for reading cursive scripts which has an architecture inspired by the behavior of human reading and perceptual concepts. The scope of this study is limited to offline recognition of isolated cursive words. First, this paper describes McClelland and Rumelharts reading model, which formed the basis of the system. The methods behavior is presented, followed by the main original contributions of our model which are: the development of a new technique for baseline extraction, an architecture based on the chosen reading model (hierarchical, parallel, with local representation and interactive activation mechanism), the use of significant perceptual features in word recognition such as ascenders and descenders, the creation of a fuzzy position concept dealing with the uncertainty of the location of features and letters, and the adaptability of the model to words of different lengths and languages. After a description of our model, new results are presented.

Low quality document image modeling and enhancement

In order to tackle problems such as shadow- through and bleed-through, a novel defect model is developed which generates physically damaged document images. This model addresses physical degradation, such as aging and ink seepage. Based on the diffusive nature of the physical defects, the model is designed using virtual diffusion processes. Then, based on this degradation model, a restoration method is proposed and used to fix the bleed-through effect in double-sided document images using the reverse diffusion process. Subjective and objective evaluations are performed on both the degradation model and the restoration method. The experiments show promising results on both real and generated data.

Semisupervised Least Squares Support Vector Machine

The least squares support vector machine (LS-SVM), like the SVM, is based on the margin-maximization performing structural risk and has excellent power of generalization. In this paper, we consider its use in semisupervised learning. We propose two algorithms to perform this task deduced from the transductive SVM idea. Algorithm 1 is based on combinatorial search guided by certain heuristics while Algorithm 2 iteratively builds the decision function by adding one unlabeled sample at the time. In term of complexity, Algorithm 1 is faster but Algorithm 2 yields a classifier with a better generalization capacity with only a few labeled data available. Our proposed algorithms are tested in several benchmarks and give encouraging results, confirming our approach.

RSLDI: Restoration of single-sided low-quality document images

This paper addresses the problem of enhancing and restoring single-sided low-quality single-sided document images. Initially, a series of multi-level classifiers is introduced covering several levels, including the regional and content levels. These classifiers can then be integrated into any enhancement or restoration method to generalize or improve them. Based on these multi-level classifiers, we first propose a novel PDE-based method for the restoration of the degradations in single-sided document images. To reduce the local nature of PDE-based methods, we empower our method with two flow fields to play the role of regional classifiers and help in preserving meaningful pixels. Also, the new method further diffuses the background information by using a content classifier, which provides an efficient and accurate restoration of the degraded backgrounds. The performance of the method is tested on both real samples, from the Google Book Search dataset, UNESCOs Memory of the World Programme, and the Juma Al Majid (Dubai) datasets, and synthesized samples provided by our degradation model. The results are promising. The method-independent nature of the classifiers is illustrated by modifying the ICA method to make it applicable to single-sided documents, and also by providing a Bayesian binarization model.