Leonard Rothacker

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Recent HMM-based approaches to handwritten word spotting require large amounts of learning samples and mostly rely on a prior segmentation of the document. We propose to use Bag-of-Features HMMs in a patch-based segmentation-free framework that are estimated by a single sample. Bag-of-Features HMMs use statistics of local image feature representatives.(More)
Due to the great variabilities in human writing, unconstrained handwriting recognition is still considered an open research topic. Recent trends in computer vision, however, suggest that there is still potential for better recognition by improving feature representations. In this paper we focus on feature learning by estimating and applying a statistical(More)
Word spotting allows to explore document images without requiring a full transcription. In the query-by-string scenario considered in this paper, it is possible to search arbitrary keywords while only limited prior information about the documents is required. We learn context-dependent character models from a training set that is small with respect to the(More)
Cuneiform tablets are an invaluable documentation of early human history. Efforts are being made in digitizing large tablet collections for preserving their content and making them available to a global research community. However, there are hardly any automated computer aided methods for supporting philologists in their analysis. In this paper we present(More)
Image parsing describes a very fine grained analysis of natural scene images, where each pixel is assigned a label describing the object or part of the scene it belongs to. This analysis is a keystone to a wide range of applications that could benefit from detailed scene understanding, such as keyword based image search, sentence based image or video(More)
This paper presents a novel method for combining local image features and spatial information for object classification tasks using the Bag-of-Features principle. The feature descriptor is extended by additional spatial information. Hence, similar feature descriptors do not only describe similar image patches, but similar patches in roughly the same region.(More)
In this paper we present how Bag-of-Features Hidden Markov Models can be applied to printed Bangla word spotting. These statistical models allow for an easy adaption to different problem domains. This is possible due to the integration of automatically estimated visual appearance features and Hidden Markov Models for spatial sequential modeling. In our(More)
Recognizing mind maps written on a whiteboard is a challenging task due to the unconstrained handwritten text and the different graphical elements — i.e. lines, circles and arrows — available in a mind map. In this paper we propose a prototype system to recognize and visualize such mind maps written on whiteboards. After the image acquisition by a camera, a(More)
Handwritten historical documents pose extremely challenging problems for automatic analysis. This is due to the high variability observed in handwritten script, the use of writing styles and script types unknown today, the frequently lacking orthographic standardization, and the degradation of the respective documents. Therefore, it is currently out of(More)
The Bag-of-Features paradigm has enjoyed great success in computer vision as well as document image analysis applications. By far the most common approach here is to power the Bag-of-Features pipeline with SIFT descriptors which are then clustered into a visual vocabulary using Lloyd’s algorithm. In contrast to using handcrafted descriptors, many researches(More)