Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

Handwriting Recognition
The RWTH-OCR system is based on the open-source speech recognition framework RWTH-
ASR - The RWTH Aachen University Speech Recognition System, which has been extended
by video and image processing methods.
RWTH developed a novel confidence-based discriminative training for handwriting
recognition. In particular, a writer adaptation approach for an HMM based Arabic
handwriting recognition system to handle different handwriting styles and their variations has
been presented in TOCITE-ICDAR-DT.
All proposed methods were evaluated on the IFN/ENIT Arabic handwriting database. In
particular, and to the best of our knowledge, the presented results could outperform all error
rates reported in the literature. The approach presented in TOCITE-ICDAR-DT ranked third
at the ICDAR 2009 Arabic Handwriting Recognition Competition. In comparison to a
preliminary evaluation of the RWTH-OCR system in 2008, the official results from 2009
show significant improvements.
Face Recognition
An interest-point based extraction of local features is widely used in object recognition tasks.
Recently, a comparative study in 2008 has shown the superior performance of local features
for face recognition in unconstrained environments. Due to the global integration of Speeded
Up Robust Features (SURF), the authors claim that it stays more robust to various image
perturbations than the more locally operating SIFT descriptor.
An interest point based feature extraction leads to sparse description of the image in
comparison to grid-based dense description. Furthermore the interest points are not stable
enough and might change depending on facial expressions.
However, no detailed analysis for a SURF based face recognition has been presented so far.
RWTH provides in TOCITE-BMVC a detailed analysis of the SURF descriptors for face
recognition, and investigate whether rotation invariant descriptors are helpful for face
recognition.
Image Distortion Models
The Euclidean distance has been successfully used e.g. in optical character and object
recognition and has been extended by different methods. As the Euclidean distance does not
account for any image transformation (such as the affine transformations scaling, translation
and rotation) if they are not part of the training corpus, the tangent distance or image
distortion model are approaches to incorporate invariance with respect to certain
transformations into a classification system.
The image distortion models have been examined at the Lehrstuhl für Informatik 6 over the
last years. Since 2008, further research and more complex image distortion models are
analyzed and presented in various works.
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