A writer identification and verification system using HMM based recognizers

In this paper, an off-line, text independent system for writer identification and verification of handwritten text lines using Hidden Markov Model (HMM) based recognizers is presented. For each writer, an individual recognizer is built and trained on text lines of that writer. This results in a number of recognizers, each of which is an expert on the handwriting of exactly one writer. In the identification and verification phase, a text line of unknown origin is presented to each of these recognizers and each one returns a transcription that includes the log-likelihood score for the generated output. These scores are sorted and the resulting ranking is used for both identification and verification. Several confidence measures are defined on this ranking. The proposed writer identification and verification system is evaluated using different experimental setup

An efficient least common subgraph algorithm for video indexing

Many tasks in computer vision can be expressed as graph problems. This allows the task to be solved using a well studied algorithm, however many of these algorithms are of exponential complexity. This is a disadvantage when considered in the context of searching a database of images or videos for similarity. Work by Mesaner and Bunke (1995) has suggested a new class of graph matching algorithms which uses a priori knowledge about a database of models to reduce the time taken during online classification. This paper presents a new algorithm which extends the earlier work to detection of the largest common subgraph.<br /

Automatic gender detection using on-line and off-line information

In this paper, the problem of classifying handwritten data with respect to gender is addressed. A classification method based on Gaussian Mixture Models is applied to distinguish between male and female handwriting. Two sets of features using on-line and off-line information have been used for the classification. Furthermore, we combined both feature sets and investigated several combination strategies. In our experiments, the on-line features produced a higher classification rate than the off-line features. However, the best results were obtained with the combination. The final gender detection rate on the test set is 67.57%, which is significantly higher than the performance of the on-line and off-line system with about 64.25 and 55.39%, respectively. The combined system also shows an improved performance over human-based classification. To the best of the authors' knowledge, the system presented in this paper is the first completely automatic gender detection system which works on on-line data. Furthermore, the combination of on-line and off-line features for gender detection is investigated for the first time in the literatur

Automatic estimation of the readability of handwritten text

Publication in the conference proceedings of EUSIPCO, Lausanne, Switzerland, 200

Massively parallel rare disease genetics

A report on the 'Genomic Disorders 2011 - The Genomics of Rare Diseases' meeting, Wellcome Trust Sanger Institute, Hinxton, UK, 23-26 March 201

Motivationssteigerung im Geometrieunterricht anhand von Modellierung kompetitiver Standortplanung

Wenn Schüler den Sinn der Mathematik in der heutigen Gesellschaft verstehen, fördert dies ihre Motivation und Aufmerksamkeit im Unterricht. Anhand eines Beispiels der kompetitiven Standortplanung wurden im Rahmen verschiedener Modellierungstage Schülerergebnisse untersucht und verglichen. Der Vortrag präsentiert sowohl die Aufgaben und mathematischen Hintergründe, als auch einen Vergleich der Ergebnisse. Ein Ausblick zur Handhabung im Unterricht wird gegeben

Combining diverse systems for handwritten text line recognition

In this paper, we present a recognition system for on-line handwritten texts acquired from a whiteboard. The system is based on the combination of several individual classifiers of diverse nature. Recognizers based on different architectures (hidden Markov models and bidirectional long short-term memory networks) and on different sets of features (extracted from on-line and off-line data) are used in the combination. In order to increase the diversity of the underlying classifiers and fully exploit the current state-of-the-art in cursive handwriting recognition, commercial recognition systems have been included in the combined system, leading to a final word level accuracy of 86.16%. This value is significantly higher than the performance of the best individual classifier (81.26%

Comparing natural and synthetic training data for off-line cursive handwriting recognition

Abstrac