Marek Tabedzki

Implementation and Advanced Results on the Non-interrupted Skeletonization Algorithm

This paper is a continuation to the work in [1], in which a new algorithm for skeletonization is introduced. The algorithm given there and implemented for script and text is applied here on images like pictures, medical organs and signatures. This is very important for a lot of applications in pattern recognition, like, for example, data compression, transmission or saving. Some interesting results have been obtained and presented in this article. Comparing our results with others we can conclude that if it comes to thinning of scripts, words or sentences our method is as good as some of the latest approaches, when considering cursive script. However, when it comes to pictures, signatures or other more complicated images, our algorithm showed better and more precise results [6].

An Exploration of Keystroke Dynamics Authentication Using Non-fixed Text of Various Length

User authentication is an important problem for any restricted access computer system, especially with remote access. Constant spread of Internet makes efficient remote authentication a pressing issue with remote interaction providing additional difficulties. Authentication methods based on most biometrics techniques mostly require dedicated hardware that is unhandy for remote applications. Keystroke dynamics however requires uniquely dedicated software and no indispensable dedicated hardware. The approach proposed by the authors in this paper is aimed at efficient user authentication with keystroke dynamics using non-fixed text of various size. We have tested the approach on a small group of individuals, with data gathered in various ways: over Internet using browser-based WWW application and on local machines using dedicated applications. The obtained results cans be used as indication for future keystrokes database creation.

User Authentication for Mobile Devices

The paper is intended as a short review of user authentication problem for mobile devices. The emphasis is put on smartphones and tablets, that nowadays are very similar to miniaturized personal computers with much more sensors of various origin. The sensors are described with remarks on their usefulness for user authentication. Deficiencies of traditional user authentication methods based on knowledge are pointed out and the need for new – more secure but also comfortable – user authentication mechanisms is reasoned. Preliminary user authentication systems employing biometric features are discussed and hence the generally unused potential of biometrics for mobile devices is demonstrated.

A modified K3M thinning algorithm

Abstract The K3M thinning algorithm is a general method for image data reduction by skeletonization. It had proved its feasibility in most cases as a reliable and robust solution in typical applications of thinning, particularly in preprocessing for optical character recognition. However, the algorithm had still some weak points. Since then K3M has been revised, addressing the best known drawbacks. This paper presents a modified version of the algorithm. A comparison is made with the original one and two other thinning approaches. The proposed modification, among other things, solves the main drawback of K3M, namely, the results of thinning an image after rotation with various angles.

Signature System Based on Extended Shape Context Descriptors

In this paper we present a system for handwritten signature identification and verification based on Shape Context Descriptors. The input to the system are binarized images of handwritten signatures. At preprocessing stage each signature image is thinned using KMM algorithm in order to obtained 1-pixel wide skeleton. The feature vector is built from Shape Context Descriptors computed for selected points on skeletonized signature line. The process of identification and verification is based on the distance measure that uses Shape Context Descriptors. The experiments were conducted on signature images from GPDS database available for non-commercial research.

Bengali Printed Character Recognition - A New Approach

This paper presents a new method for Bengali character recognition based on view-based approach. Both the top-bottom and the lateral view-based approaches have been considered. A layer-based methodology in modification of the basic view-based processing has been proposed. This facilitates handling of unequal logical partitions. The document image is acquired and segmented to extract out the text lines, words, and letters. The whole image of the individual characters is taken as the input to the system. The character image is put into a bounding box and resized whenever necessary. The view-based approach is applied on the resultant image and the characteristic points are extracted from the views after some preprocessing. These points are then used to form a feature vector that represents the given character as a descriptor. The feature vectors have been classified with the aid of k-NN classifier using Dynamic Time Warping (DTW) as a distance measure. A small dataset of some of the compound characters has also been considered for recognition. The promising results obtained so far encourage the authors for further work on handwritten Bengali scripts.