Incorporation of relational information in feature representation for online handwriting recognition of Arabic characters

Interest in online handwriting recognition is increasing due to market demand for both improved performance and for extended supporting scripts for digital devices. Robust handwriting recognition of complex patterns of arbitrary scale, orientation and location is elusive to date because reaching a target recognition rate is not trivial for most of the applications in this field. Cursive scripts such as Arabic and Persian with complex character shapes make the recognition task even more difficult. Challenges in the discrimination capability of handwriting recognition systems depend heavily on the effectiveness of the features used to represent the data, the types of classifiers deployed and inclusive databases used for learning and recognition which cover variations in writing styles that introduce natural deformations in character shapes. This thesis aims to improve the efficiency of online recognition systems for Persian and Arabic characters by presenting new formal feature representations, algorithms, and a comprehensive database for online Arabic characters. The thesis contains the development of the first public collection of online handwritten data for the Arabic complete-shape character set. New ideas for incorporating relational information in a feature representation for this type of data are presented. The proposed techniques are computationally efficient and provide compact, yet representative, feature vectors. For the first time, a hybrid classifier is used for recognition of online Arabic complete-shape characters based on the idea of decomposing the input data into variables representing factors of the complete-shape characters and the combined use of the Bayesian network inference and support vector machines. We advocate the usefulness and practicality of the features and recognition methods with respect to the recognition of conventional metrics, such as accuracy and timeliness, as well as unconventional metrics. In particular, we evaluate a feature representation for different character class instances by its level of separation in the feature space. Our evaluation results for the available databases and for our own database of the characters' main shapes confirm a higher efficiency than previously reported techniques with respect to all metrics analyzed. For the complete-shape characters, our techniques resulted in a unique recognition efficiency comparable with the state-of-the-art results for main shape characters

Feature design and lexicon reduction for efficient offline handwriting recognition

This thesis establishes a pattern recognition framework for offline word recognition systems. It focuses on the image level features because they greatly influence the recognition performance. In particular, we consider two complementary aspects of prominent features impact: lexicon reduction and the actual recognition. The first aspect, lexicon reduction, consists in the design of a weak classifier which outputs a set of candidate word hypotheses given a word image. Its main purpose is to reduce the recognition computational time while maintaining (or even improving) the recognition rate. The second aspect is the actual recognition system itself. In fact, several features exist in the literature based on different fields of research, but no consensus exists concerning the most promising ones. The goal of the proposed framework is to improve our understanding of relevant features in order to build better recognition systems. For this purpose, we addressed two specific problems: 1) feature design for lexicon reduction (application to Arabic script), and 2) feature evaluation for cursive handwriting recognition (application to Latin and Arabic scripts). Few methods exist for lexicon reduction in Arabic script, unlike Latin script. Existing methods use salient features of Arabic words such as the number of subwords and diacritics, but totally ignore the shape of the subwords. Therefore, our first goal is to perform lexicon reductionn based on subwords shape. Our approach is based on shape indexing, where the shape of a query subword is compared to a labeled database of sample subwords. For efficient comparison with a low computational overhead, we proposed the weighted topological signature vector (W-TSV) framework, where the subword shape is modeled as a weighted directed acyclic graph (DAG) from which the W-TSV vector is extracted for efficient indexing. The main contributions of this work are to extend the existing TSV framework to weighted DAG and to propose a shape indexing approach for lexicon reduction. Good performance for lexicon reduction is achieved for Arabic subwords. Nevertheless, the performance remains modest for Arabic words. Considering the results of our first work on Arabic lexicon reduction, we propose to build a new index for better performance at the word level. The subword shape and the number of subwords and diacritics are all important components of Arabic word shape. We therefore propose the Arabic word descriptor (AWD) which integrates all the aforementioned components. It is built in two steps. First, a structural descriptor (SD) is computed for each connected component (CC) of the word image. It describes the CC shape using the bag-of-words model, where each visual word represents a different local shape structure. Then, the AWD is formed by concatenating the SDs using an efficient heuristic, implicitly discriminating between subwords and diacritics. In the context of lexicon reduction, the AWD is used to index a reference database. The main contribution of this work is the design of the AWD, which integrates lowlevel cues (subword shape structure) and symbolic information (subword counts and diacritics) into a single descriptor. The proposed method has a low computational overhead, it is simple to implement and it provides state-of-the-art performance for lexicon reduction on two Arabic databases, namely the Ibn Sina database of subwords and the IFN/ENIT database of words. The last part of this thesis focuses on features for word recognition. A large body of features exist in the literature, each of them being motivated by different fields, such as pattern recognition, computer vision or machine learning. Identifying the most promising approaches would improve the design of the next generation of features. Nevertheless, because they are based on different concepts, it is difficult to compare them on a theoretical ground and efficient empirical tools are needed. Therefore, the last objective of the thesis is to provide a method for feature evaluation that assesses the strength and complementarity of existing features. A combination scheme has been designed for this purpose, in which each feature is evaluated through a reference recognition system, based on recurrent neural networks. More precisely, each feature is represented by an agent, which is an instance of the recognition system trained with that feature. The decisions of all the agents are combined using a weighted vote. The weights are jointly optimized during a training phase in order to increase the weighted vote of the true word label. Therefore, they reflect the strength and complementarity of the agents and their features for the given task. Finally, they are converted into a numerical score assigned to each feature, which is easy to interpret under this combination model. To the best of our knowledge, this is the first feature evaluation method able to quantify the importance of each feature, instead of providing a ranking based on the recognition rate. Five state-of-the-art features have been tested, and our results provide interesting insight for future feature design

Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods

A Handwritten Character Recognition Method Based on Unconstrained Elastic Matching and Eigen-Deformations

A fast elastic matching based handwritten character recognition method is investigated. In the present method, an unconstrained elastic matching technique, where the matching is optimized locally and individually on each pixel, is utilized together with its a posteriori evaluation based on the eigen-deformations of handwritten characters. Our experimental results show that high recognition rates can be attained by the present method with feasible computations

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study