This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation

Rapid prototyping of gesture interaction for emerging touch platforms requires that developers have access to fast, simple, and accurate gesture recognition approaches. The $-family of recognizers ($1, $N) addresses this need, but the current most advanced of these, $N-Protractor, has significant memory and execution costs due to its combinatoric gesture representation approach. We present $P, a new member of the $-family, that remedies this limitation by considering gestures as clouds of points. $P performs similarly to $1 on unistrokes and is superior to $N on multistrokes. Specifically, $P delivers >99% accuracy in user-dependent testing with 5+ training samples per gesture type and stays above 99% for user-independent tests when using data from 10 participants. We provide a pseudocode listing of $P to assist developers in porting it to their specific platform and a "cheat sheet" to aid developers in selecting the best member of the $-family for their specific application needs.

https://faculty.washington.edu/wobbrock/pubs/icmi-12.pdf

Gestures as point clouds: a $P recognizer for user interface prototypes

Faster retrieval with a two-pass dynamic-time-warping lower bound

As mobile devices like the iPad and iPhone become increasingly commonplace, touchscreen interactions are quickly overtaking other interaction methods in terms of frequency and experience for many users. However, most of these devices have been designed for the general, typical user. Trends indicate that children are using these devices (either their parents' or their own) for entertainment or learning activities. Previous work has found key differences in how children use touch and surface gesture interaction modalities vs. adults. In this paper, we specifically examine the impact of these differences in terms of automatically and reliably understanding what kids meant to do. We present a study of children and adults performing touch and surface gesture interaction tasks on mobile devices. We identify challenges related to (a) intentional and unintentional touches outside of onscreen targets and (b) recognition of drawn gestures, that both indicate a need to design tailored interaction for children to accommodate and overcome these challenges.

Interaction and recognition challenges in interpreting children's touch and gesture input on mobile devices

Iconic and multi-stroke gesture recognition

https://repository.ubn.ru.nl/bitstream/handle/2066/73060/73060_aut.pdf

Dynamic time warping: a new method in the study of poor handwriting.

A well-established task in forensic writer identification focuses on the comparison of prototypical character shapes (allographs) present in handwriting. In order for a computer to perform this task convincingly, it should yield results that are plausible and understandable to the human expert. Trajectory matching is a well-known method to compare two allographs. This paper assesses a promising technique for so-called human-congruous trajectory matching, called Dynamic Time Warping (DTW). In the first part of the paper, an experiment is described that shows that DTW yields results that correspond to the expectations of human users. Since DTW requires the dynamics of the handwritten trace, the "online" dynamic allograph trajectories need to be extracted from the "offline" scanned documents. In the second part of the paper, an automatic procedure to perform this task is described. Images were generated from a large online dataset that provides the true trajectories. This allows for a quantitative assessment of the trajectory extraction techniques rather than a qualitative discussion of a small number of examples. Our results show that DTW can significantly improve the results from trajectory extraction when compared to traditional techniques.

/pdf/automatic-allograph-matching-in-forensic-writer-1a95tslc1j.pdf

Automatic allograph matching in forensic writer identification

This paper discusses the use of Dynamic Time Warping (DTW) for visually perceptive and intuitive character recognition. In particular in forensic document examination, techniques are required that yield results matching a human user's expectations. In our approach, the goal is to retrieve a set of best matching allographic prototypes based on a query input character. Since DTW compares each pair of closest points from two trajectories, our assumption is that it may resemble most of the pair-wise coordinate comparisons employed by humans. In order to assess our ideas, we have set up a human factors experiment in which two variants of DTW are compared to a state-ofthe- art character classifier, HCLUS. A number of 25 subjects judged the recognition results of these three classifiers for 130 queries. As a result, one particular implementation of DTW was significantly rated as the best system. Future research will combine these promising new findings with techniques that employ other distinctive features like those used by human experts. This research is embedded in the Dutch TRIGRAPH project, which pursues the design of forensic document examination techniques based on expert knowledge.

Using Dynamic Time Warping for Intuitive Handwriting Recognition

The goal of this paper is to announce collection of handwritten sketches containing These data have recently been collected the goal to develop and assess pen technologies for the domain of crisis envisaged scenarios, users can use information about the location of certain occurrence of certain events, on a digital map. Typically, these sketches representations of drawings that are not contained available databases, which mainly contain We report on our classification performances these data and make the data freely handwriting recognition community.

R.M.J. Niels

Papers

Iconic and multi-stroke gesture recognition

Dynamic time warping: a new method in the study of poor handwriting.

Automatic allograph matching in forensic writer identification

Using Dynamic Time Warping for Intuitive Handwriting Recognition

The NicIcon database of handwritten icons