Ralph Niels

Iconic and multi-stroke gesture recognition

Many handwritten gestures, characters, and symbols comprise multiple pendown strokes separated by penup strokes. In this paper, a large number of features known from the literature are explored for the recognition of such multi-stroke gestures. Features are computed from a global gesture shape. From its constituent strokes, the mean and standard deviation of each feature are computed. We show that using these new stroke-based features, significant improvements in classification accuracy can be obtained between 10% and 50% compared to global feature representations. These results are consistent over four different databases, containing iconic pen gestures, handwritten symbols, and upper-case characters. Compared to two other multi-stroke recognition techniques, improvements between 25% and 39% are achieved, averaged over all four databases.

Dynamic time warping applied to Tamil character recognition

This paper describes the use of dynamic time warping (DTW) for classifying handwritten Tamil characters. Since DTW can match characters of arbitrary length, it is particularly suited for this domain. We built a prototype based classifier that uses DTW both for generating prototypes and for calculating a list of nearest prototypes. Prototypes were automatically generated and selected. Two tests were performed to measure the performance of our classifier in a writer dependent, and in a writer independent setting. Furthermore, several strategies were developed for rejecting uncertain cases. Two different rejection variables were implemented and using a Monte Carlo simulation, the performance of the system was tested in various configurations. The results are promising and show that the classifier can be of use in both writer dependent and writer independent automatic recognition of handwritten Tamil characters.

Towards robust writer verification by correcting unnatural slant

Slant is a salient feature of Western handwriting and it is considered to be an important writer-specific feature. In disguised handwriting however, slant is often modified. It was tested whether slant is indeed an important factor and it was tested whether the distorting effect of deliberate slant change can be countered by a simple shear transform. This was done in two off-line writer verification experiments in image processing conditions of slant elimination and slant correction. The experiments were performed using three features based on statistical pattern recognition, including the state-of-the-art features Fraglets and Hinge. A new public dataset was created and used, containing natural and slanted handwriting by 47 writers. A striking result is that the average natural slant value is much less important for biometric systems than is usually assumed: eliminating slant yields just a 1-5% performance loss. A second result is that the effects of deliberate slant change cannot be fully countered by a simple shear transform: it raises performance on the distorted handwriting from 53-68% to 64-90%, but this is still lower than normal operation on natural handwriting: 97-100%.

Generating Copybooks from Consistent Handwriting Styles

The automatic extraction of handwriting styles is an important process that can be used for various applications in the processing of handwriting. We propose a novel method that employs hierarchical clustering to explore prominent clusters of handwriting. So-called membership vectors are introduced to describe the handwriting of a writer. Each membership vector reveals the frequency of occurrence of prototypical characters in a writers handwriting. By clustering these vectors, consistent handwriting styles can be extracted, similar to the exemplar handwritings documented in copybooks. The results presented here are challenging. The most prominent handwriting styles detected correspond to the broad style categories cursive, mixed, and print.

Design Issues for Pen-Centric Interactive Maps

Recent advances in interactive pen-aware systems, pattern recognition technologies, and human–computer interaction have provided new opportunities for pen-based communication between human users and intelligent computer systems. Using interactive maps, users can annotate pictorial or cartographic information by means of pen gestures and handwriting. Interactive maps may provide an efficient means of communication, in particular in the envisaged contexts of crisis management scenarios, which require robust and effective exchange of information. This information contains, e.g., the location of objects, the kind of incidents, or the indication of route alternatives. When considering human interactions in these contexts, various pen input modes are involved, like handwriting, drawing, and sketching. How to design the required technology for grasping the intentions of the user based on these pen inputs remains an elusive challenge, which is discussed in this chapter. Aspects like the design of a suitable set of pen gestures, data collection in the context of the envisaged scenarios, and the development of distinguishing features and pattern recognition technologies for robustly recognizing pen input from varying modes are described. These aspects are illustrated by presenting our recent results on the development of interactive maps within the framework of the ICIS project on crisis management systems.