Template Based Recognition of On-Line Handwriting

Software for recognition of handwriting has been available for several decades now and research on the subject have produced several different strategies for producing competitive recognition accuracies, especially in the case of isolated single characters. The problem of recognizing samples of handwriting with arbitrary connections between constituent characters (emph{unconstrained handwriting}) adds considerable complexity in form of the segmentation problem. In other words a recognition system, not constrained to the isolated single character case, needs to be able to recognize where in the sample one letter ends and another begins. In the research community and probably also in commercial systems the most common technique for recognizing unconstrained handwriting compromise Neural Networks for partial character matching along with Hidden Markov Modeling for combining partial results to string hypothesis. Neural Networks are often favored by the research community since the recognition functions are more or less automatically inferred from a training set of handwritten samples. From a commercial perspective a downside to this property is the lack of control, since there is no explicit information on the types of samples that can be correctly recognized by the system. In a template based system, each style of writing a particular character is explicitly modeled, and thus provides some intuition regarding the types of errors (confusions) that the system is prone to make. Most template based recognition methods today only work for the isolated single character recognition problem and extensions to unconstrained recognition is usually not straightforward. This thesis presents a step-by-step recipe for producing a template based recognition system which extends naturally to unconstrained handwriting recognition through simple graph techniques. A system based on this construction has been implemented and tested for the difficult case of unconstrained online Arabic handwriting recognition with good results

Applying Machine Learning on RSRP-based Features for False Base Station Detection

False base stations -- IMSI catchers, Stingrays -- are devices that impersonate legitimate base stations, as a part of malicious activities like unauthorized surveillance or communication sabotage. Detecting them on the network side using 3GPP standardized measurement reports is a promising technique. While applying predetermined detection rules works well when an attacker operates a false base station with an illegitimate Physical Cell Identifiers (PCI), the detection will produce false negatives when a more resourceful attacker operates the false base station with one of the legitimate PCIs obtained by scanning the neighborhood first. In this paper, we show how Machine Learning (ML) can be applied to alleviate such false negatives. We demonstrate our approach by conducting experiments in a simulation setup using the ns-3 LTE module. We propose three robust ML features (COL, DIST, XY) based on Reference Signal Received Power (RSRP) contained in measurement reports and cell locations. We evaluate four ML models (Regression Clustering, Anomaly Detection Forest, Autoencoder, and RCGAN) and show that several of them have a high precision in detection even when the false base station is using a legitimate PCI. In our experiments with a layout of 12 cells, where one cell acts as a moving false cell, between 75-95\% of the false positions are detected by the best model at a cost of 0.5\% false positives.Comment: 9 pages,5 figure, 3 tables, 2 algorithm

Pleuropulmonary pathologies in the early phase of acute pancreatitis correlate with disease severity

Background Respiratory failure worsens the outcome of acute pancreatitis (AP) and underlying factors might be early detectable. Aims To evaluate the prevalence and prognostic relevance of early pleuropulmonary pathologies and pre-existing chronic lung diseases (CLD) in AP patients. Methods Multicentre retrospective cohort study. Caudal sections of the thorax derived from abdominal contrast enhanced computed tomography (CECT) performed in the early phase of AP were assessed. Independent predictors of severe AP were identified by binary logistic regression analysis. A one-year survival analysis using Kaplan-Meier curves and log rank test was performed. Result 358 patients were analysed, finding pleuropulmonary pathologies in 81%. CECTs were performed with a median of 2 days (IQR 1-3) after admission. Multivariable analysis identified moderate to severe or bilateral pleural effusions (PEs) (OR = 4.16, 95%CI 2.05-8.45, p Conclusions Increasing awareness of the prognostic impact of large and bilateral PEs and pre-existing CLD could facilitate the identification of patients at high risk for severe AP in the early phase and thus improve their prognosis.Peer reviewe

In Vitro Models for Studying Secondary Plant Metabolite Digestion and Bioaccessibility

There is an increased interest in secondary plant metabolites, such as polyphenols and carotenoids, due to their proposed health benefits. Much attention has focused on their bioavailability, a prerequisite for further physiological functions. As human studies are time consuming, costly, and restricted by ethical concerns, in vitro models for investigating the effects of digestion on these compounds have been developed and employed to predict their release from the food matrix, bioaccessibility, and assess changes in their profiles prior to absorption. Most typically, models simulate digestion in the oral cavity, the stomach, the small intestine, and, occasionally, the large intestine. A plethora of models have been reported, the choice mostly driven by the type of phytochemical studied, whether the purpose is screening or studying under close physiological conditions, and the availability of the model systems. Unfortunately, the diversity of model conditions has hampered the ability to compare results across different studies. For example, there is substantial variability in the time of digestion, concentrations of salts, enzymes, and bile acids used, pH, the inclusion of various digestion stages; and whether chosen conditions are static (with fixed concentrations of enzymes, bile salts, digesta, and so on) or dynamic (varying concentrations of these constituents). This review presents an overview of models that have been employed to study the digestion of both lipophilic and hydrophilic phytochemicals, comparing digestive conditions in vitro and in vivo and, finally, suggests a set of parameters for static models that resemble physiological conditions

Core Points - Variable and Reduced Parameterization for Symbol Recognition

Recent research in the field of on-line handwriting recognition has been focused on statistical systems such as Hidden Markov Models, Neural Networks or a combination of these. There are however merits of employing an approach based on template matching. The first part of this thesis presents a new strategy for parameterization of on-line handwritten character samples. A novel efficient template matching method enabled by this parameterization is also proposed. In consecutive chapters of the thesis it is also shown that the proposed structural parameterization enables an effective application of template matching methods to the recognition of cursive script. Ambiguity of the shapes of individual characters in unconstrained cursive handwriting necessitates dictionary interaction for real applications. A fast technique for applying dictionary information to the language independent graph approach has also been developed. A large data set of on-line cursive writing has been collected and the developed system for mixed and cursive on-line handwriting recognition has been shown to produce state of the art results on this data set. One of the obvious potential weaknesses of a structural parameterization technique such as the one presented in this thesis is its sensitivity to digital noise in the form of superfluous coordinates. Possible remedies to deal with such effects have also been studied

Frame Deformation Energy Matching of On-line Handwritten Characters

The coarse to fine search methodology is frequently applied to a wide variety of problems in computer vision. In this paper it is shown that this strategy can be used to enhance the recognition of on-line handwritten characters. Some explicit knowledge about the structure of a handwritten character can be obtained through a structural parameterization. The Frame Deformation Energy matching (FDE) method is a method optimized to include such knowledge in the discrimination process. This paper presents a novel parameterization strategy, the Djikstra Curve Maximization (DCM) method, for the segments of the structural frame. Since this method distributes points unevenly on each segment, point-to-point matching strategies are not suitable. A new distance measure for these segment-to-segment comparisons have been developed. Experiments have been conducted with various settings for the new FDE on a large data set both with a single model matching scheme and with a kNN type template matching scheme. The results reveal that the FDE even in an ad hoc implementation is a robust matching method with recognition results well comparing to the existing state-of-the-art methods

Prototype Selection Methods for On-line HWR

http://www.suvisoft.comPrototype matching strategies such as DP-matching are powerful methods for character recognition problems with very large number of classes such as on-line Chinese character recognition. As for many problems with a large number of classes data is normally comparatively scarce for on-line Chinese characters and therefore prototype se- lection methods for use under these circumstances need to be robust against over-training. The methods investigated in this paper are incremental in the sense that all proto- type additions are based on the number of misclassifica- tions caused by the current database. This may be highly beneficial as it is possible to use the methods on databases that have undergone other optimization methods. Experi- ments have been conducted on the HANDS Kanji data and it reveals some interesting results