Visual input for pen-based computers

The design and implementation of a camera-based, human-computer interface for acquisition of handwriting is presented. The camera focuses on a standard sheet of paper and images a common pen; the trajectory of the tip of the pen is tracked and the contact with the paper is detected. The recovered trajectory is shown to have sufficient spatio-temporal resolution and accuracy to enable handwritten character recognition. More than 100 subjects have used the system and have provided a large and heterogeneous set of examples showing that the system is both convenient and accurate

Visual identification by signature tracking

We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics

Visual signature verification using affine arc-length

Signatures can be acquired with a camera-based system with enough resolution to perform verification. This paper presents the performance of a visual-acquisition signature verification system, emphasizing on the importance of the parameterisation of the signature in order to achieve good classification results. A technique to overcome the lack of examples in order to estimate the generalization error of the algorithm is also described

Computer vision based unistroke keyboard system and mouse for the handicapped

In this paper, a unistroke keyboard based on computer vision is described for the handicapped. The keyboard can be made of paper or fabric containing an image of a keyboard, which has an upside down U-shape. It can even be displayed on a computer screen. Each character is represented by a non-overlapping rectangular region on the keyboard image and the user enters a character by illuminating a character region with a laser pointer. The keyboard image is monitored by a camera and illuminated key locations are recognized. During the text entry process the user neither have to turn the laser light off nor raise the laser light from the keyboard. A disabled person who has difficulty using his/her hands may attach the laser pointer to an eyeglass and easily enter text by moving his/her head to point the laser beam on a character location. In addition, a mouse-like device can be developed based on the same principle. The user can move the cursor by moving the laser light on the computer screen which is monitored by a camera. © 2003 IEEE

Vision-based continuous Graffiti™-like text entry system

It is now possible to design real-time, low-cost computer version systems even in personal computers due to the recent advances in electronics and the computer industry. Due to this reason, it is feasible to develop computer-vision-based human-computer interaction systems. A vision-based continuous Graffiti™-like text entry system is presented. The user sketches characters in a Griffiti™-like alphabet in a continuous manner on a flat surface using a laser pointer. The beam of the laser pointer is tracked on the image sequences captured by a camera, and the corresponding written word is recognized from the extracted trace of the laser beam. © 2004 Society of Photo-Optical Instrumentation Engineers

Decomposition of human motion into dynamics-based primitives with application to drawing tasks

Using tools from dynamical systems and systems identification, we develop a framework for the study of primitives for human motion, which we refer to as movemes. The objective is understanding human motion by decomposing it into a sequence of elementary building blocks that belong to a known alphabet of dynamical systems. We develop a segmentation and classification algorithm in order to reduce a complex activity into the sequence of movemes that have generated it. We test our ideas on data sampled from five human subjects who were drawing figures using a computer mouse. Our experiments show that we are able to distinguish between movemes and recognize them even when they take place in activities containing an unspecified number of movemes

Online signature verification techniques

Signature is a behavioral biometric: it is not based on the physical properties, such as fingerprint or face, of the individual, but behavioral ones. Signature verification is split into two according to the available data in the input. Offline (static) signature verification takes as input the image of a signature and is useful in automatic verification of signatures found on bank checks and documents. Online (dynamic) signature verification uses signatures that are captured by pressure-sensitive tablets that extract dynamic properties of a signature in addition to its shape. The purpose of project is to develop an authentication system based on personal signatures. Signature verification is an important research topic in the area of biometric authentication. In this project the work is done in such a way that the signatures are captured using WEBCAM. A visual-based online signature verification system in which the signer’s pen tip is tracked. The data acquisition of the system consists of only low-cost cameras (webcams) and does not need special equipment such as an electronic tablet. Online signature data is obtained from the images captured by the webcams by tracking the pen tip. The pen tip tracking is implemented by the Sequential Monte Carlo method in real time. Then, the distance between the input signature data and reference signature data enrolled in advance is computed using Dynamic Time Warping (DTW). Finally, the input signature is classified as genuine or a forgery by comparing the distance with a threshold

Visual Input for Pen-Based Computers

Handwriting may be captured using a video camera, rather than the customary pressure-sensitive tablet. This paper presents a simple system based on correlation and recursive prediction methods that can track the tip of the pen in real time with sufficient spatio-temporal resolution and accuracy to enable handwritten character recognition. The system is tested on a large and heterogeneous set of examples and its performance is compared to that of three human operators and a commercial high-resolution pressure-sensitive tablet. Keywords: Systems and applications, Active and real-time vision, Handwriting acquisition. 1 Introduction and Motivation Computers are getting faster and smaller every day. Notebook and laptop personal computers, pen-based computers and personal organizers, are designed to be as small and portable as possible. While until now their size was limited by hard disk, memory chips, battery and power supplies, the lower bound is now increasingly dependent on the size o..