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Automated Ground Truth Data Generation for Newspaper Document images

Automated Ground Truth Data Generation for Newspaper Documen...

引用

International Conference on Document Analysis and recognition

作者： Thomas Strecker Joost van Beusekom Sahin Albayrak Thomas M. Breuel DAI Labor Technical University Berlin Germany Image Understanding and Pattern Recognition (IUPR) Research Group Technical University of Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (纸本)9781424445004

In document image understanding, public datasets with ground-truth are an important part of scientific work. They are not only helpful for developing new methods, but also provide a way of comparing performance. Generating these datasets, however, is time consuming and cost-intensive work, requiring a lot of manual effort. In this paper we both propose a way to semi-automatically generate ground-truthed datasets for newspapers and provide a comprehensive dataset. The focus of this paper is layout analysis ground truth. The proposed two step approach consists of a module which automatically creates layouts and an image matching module which allows to map the ground truth information from the synthetic layout to the scanned version. In the first step, layouts are generated automatically from a news corpus. The output consists of a digital newspaper (PDF file) and an XML file containing geometric and logical layout information. In the second step, the PDF files are printed, scanned and aligned with the synthetic image obtained by rendering the PDF. Finally, the geometric and logical layout ground truth is mapped onto the scanned image.

关键词： image segmentation pattern recognition Text analysis image analysis pattern analysis image recognition Artificial intelligence Focusing image matching XML

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Automated OCR Ground Truth Generation

Automated OCR Ground Truth Generation

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Joost van Beusekom Faisal Shafait Thomas M. Breuel Image Understanding and Pattern Recognition (IUPR) Research Group German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany Image Understanding and Pattern Recognition (IUPR) Research Group Technical University of Kaiserslautern Germany

Most optical character recognition (OCR) systems need to be trained and tested on the symbols that are to be recognized. Therefore, ground truth data is needed. This data consists of character images together with their ASCII code. Among the approaches for generating ground truth of real world data, one promising technique is to use electronic version of the scanned documents. Using an alignment method, the character bounding boxes extracted from the electronic document are matched to the scanned image. Current alignment methods are not robust to different similarity transforms. They also need calibration to deal with non-linear local distortions introduced by the printing/scanning process. In this paper we present a significant improvement over existing methods, allowing to skip the calibration step and having a more accurate alignment, under all similarity transforms. Our method finds a robust and pixel accurate scanner independent alignment of the scanned image with the electronic document, allowing the extraction of accurate ground truth character information. The accuracy of the alignment is demonstrated using documents from the UW3 dataset. The results show that the mean distance between the estimated and the ground truth character bounding box position is less than one pixel.

关键词： Optical character recognition software Character recognition Data mining Robustness Calibration Nonlinear optics Optical distortion System testing Nonlinear distortion Printing

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Structural Mixtures for Statistical Layout Analysis

Structural Mixtures for Statistical Layout Analysis

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Faisal Shafait Joost van Beusekom Daniel Keysers Thomas M. Breuel Image Understanding and Pattern Recognition (IUPR) Research Group German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany Technische Universitat Kaiserslautern Kaiserslautern Rheinland-Pfalz DE Image Understanding and Pattern Recognition (IUPR) Research Group Technical University of Kaiserslautern Germany

A key limitation of current layout analysis methods is that they rely on many hard-coded assumptions about document layouts and can not adapt to new layouts for which the underlying assumptions are not satisfied. Another major drawback of these approaches is that they do not return confidence scores for their outputs. These problems pose major challenges in large scale digitization efforts where a large number of different layouts need to be handled and manual inspection of the results on each individual page is not feasible. This paper presents a novel statistical approach to layout analysis that aims at solving the above mentioned problems for Manhattan layouts. The presented approach models known page layouts as a structural mixture model. A probabilistic matching algorithm is presented that gives multiple interpretations of input layout with associated probabilities. First experiments on documents from the publicly available MARG dataset achieved below 5%error rate for geometric layout analysis.

关键词： Books image segmentation image analysis pattern analysis Text analysis Large-scale systems pattern recognition Artificial intelligence Inspection Probability

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Background variability modeling for statistical layout analysis

Background variability modeling for statistical layout analy...

引用

International Conference on pattern recognition

Geometric layout analysis plays an important role in document image understanding. Many algorithms known in literature work well on standard document images, achieving high text line segmentation accuracy on the UW-III dataset. These algorithms rely on certain assumptions about document layouts, and fail when their underlying assumptions are not met. Also, they do not provide confidence scores for their output. These two problems limit the usefulness of general purpose layout analysis methods in large scale applications. In this contribution, we propose a statistically motivated model-based trainable layout analysis system that allows assumption-free adaptation to different layout types and produces likelihood estimates of the correctness of the computed page segmentation. The performance of our approach is tested on a subset of the Google 1000 books dataset where it achieved a text line segmentation accuracy of 98.4% on layouts where other general-purpose algorithms failed to do a correct segmentation.

关键词： Training data Solid modeling image analysis pattern analysis Stochastic processes pattern recognition Artificial intelligence image segmentation Large-scale systems Testing

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Bayes Optimal DDoS Mitigation by Adaptive History-Based IP Filtering

Bayes Optimal DDoS Mitigation by Adaptive History-Based IP F...

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International Conference on Networking (ICN)

作者： Markus Goldstein Christoph Lampert Matthias Reif Armin Stahl Thomas Breuel Research Group Image Understanding and Pattern Recognition (IUPR) German Research Center for Artificial Intelligence Kaiserslautern Germany Department of Computer Science Technical University of Kaiserslautern Kaiserslautern Germany

Distributed denial of service (DDoS) attacks are today the most destabilizing factor in the global internet and there is a strong need for sophisticated solutions. We introduce a formal statistical framework and derive a Bayes optimal packet classifier from it. Our proposed practical algorithm "adaptive history-based IP filtering" (AHIF) mitigates DDoS attacks near the victim and outperforms existing methods by at least 32% in terms of collateral damage. Furthermore, it adjusts to the strength of an ongoing attack and ensures availability of the attacked server. In contrast to other adaptive solutions, firewall rulesets used to resist an attack can be precalculated before an attack takes place. This ensures an immediate response in a DDoS emergency. For evaluation, simulated DDoS attacks and two real-world user traffic datasets are used.

关键词： Adaptive filters Computer crime Information filtering Information filters Web and internet services Web server IP networks Traffic control Law Legal factors

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Example-Based Logical Labeling of Document Title Page images

Example-Based Logical Labeling of Document Title Page Images

引用

International Conference on Document Analysis and recognition

作者： J. van Beusekom D. Keysers F. Shafait T. Breuel Technical University of Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany Image Understanding and Pattern Recognition (IUPR) Research Group Technical University of Kaiserslautern Germany

This paper presents a flexible and effective example- based approach for labeling title pages which can be used for automated extraction of bibliographic data. The labels of interest are "title", "author", "abstract" and "affiliation". The method takes a set of labeled document layouts and a single unlabeled document layout as input and finds the best matching layout in the set. The labels of this layout are used to label the new layout. The similarity measure for layouts combines structural layout similarity and textural similarity on the block-level. Experimental results yield accuracy rates from 94.8% to 99.6% obtained on the publicly available MARG dataset. This shows that our lightweight method has equivalent and partially better performance when compared to other more complex labeling methods known from the literature.

关键词： Layout Labeling Databases Adaptation models Hidden Markov models Histograms image segmentation

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引用

21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06

作者： Stahl, Armin Gabel, Thomas German Research Center Image Understanding and Pattern Recognition Group Technical University of Kaiserslautern Department of Mathematics and Computer Science Institute of Cognitive Science University of Osnabrück

ISBN: (纸本)1577352815

The definition of accurate similarity measures is a key issue of every Case-Based Reasoning application. Although some approaches to optimize similarity measures automatically have already been applied, these approaches are not suited for all CBR application domains. On the one hand, they are restricted to classification tasks. On the other hand, they only allow optimization of feature weights. We propose a novel learning approach which addresses both problems, i.e. it is suited for most CBR application domains beyond simple classification and it enables learning of more sophisticated similarity measures. Copyright © 2006, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Information retrieval

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Distance measures for layout-based document image retrieval

Distance measures for layout-based document image retrieval

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International Workshop on Document image Analysis for Libraries

作者： J. van Beusekom D. Keysers F. Shafait T.M. Breuel Image Understanding and Pattern Recognition (IUPR) Research Group German Research Center for Artificial Intelligence (DFKI) Technical University of Kaiserslautern Kaiserslautern Germany

Most methods for document image retrieval rely solely on text information to find similar documents. This paper describes a way to use layout information for document image retrieval instead. A new class of distance measures is introduced for documents with Manhattan layouts, based on a two-step procedure: First, the distances between the blocks of two layouts are calculated. Then, the blocks of one layout are assigned to the blocks of the other layout in a matching step. Different block distances and matching methods are compared and evaluated using the publicly available MARG database. On this dataset, the layout type can be determined successfully in 92.6% of the cases using the best distance measure in a nearest neighbor classifier. The experiments show that the best distance measure for this task is the overlapping area combined with the Manhattan distance of the corner points as block distance together with the minimum weight edge cover matching

关键词： image retrieval Databases Optical character recognition software Algorithm design and analysis Information retrieval pattern recognition Artificial intelligence Nearest neighbor searches Area measurement Current measurement

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Pixel-Accurate Representation and Evaluation of Page Segmentation in Document images

Pixel-Accurate Representation and Evaluation of Page Segment...

引用

International Conference on pattern recognition

作者： F. Shafait D. Keysers T.M. Breuel Image Understanding and Pattern Recognition (IUPR) Research Group German Research Center for Artificial Intelligence (DFKI) Technical University of Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)0769525210

This paper presents a new representation and evaluation procedure of page segmentation algorithms and analyzes six widely-used layout analysis algorithms using the procedure. The method permits a detailed analysis of the behavior of page segmentation algorithms in terms of over- and undersegmentation at different layout levels, as well as determination of the geometric accuracy of the segmentation. The representation of document layouts relies on labeling each pixel according to its function in the overall segmentation, permitting pixel-accurate representation of layout information of arbitrary layouts and allowing background pixels to be classified as "don't care". Our representations can be encoded easily in standard color image formats like PNG, permitting easy interchange of segmentation results and ground truth

关键词： Pixel image segmentation Algorithm design and analysis Color pattern recognition image analysis pattern analysis Measurement Artificial intelligence Labeling

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Layout Analysis of Urdu Document images

Layout Analysis of Urdu Document Images

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IEEE International Conference on Multi Topic

作者： Faisal Shafait Adnan-ul-Hasan Daniel Keysers Thomas M. Breuel Image Understanding and Pattern Recognition (IUPR) research group German Research Center for Artificial Intelligence Kaiserslautern Germany Department of Computer Science Technical University of Kaiserslautern Kaiserslautern Germany

Layout analysis is a key component of an OCR system. In this paper, we present a layout analysis system for extracting text-lines in reading order from Urdu document images. For this purpose, we evaluate an existing system for Roman script text on Urdu documents and describe its methods and the main changes necessary to adapt it to Urdu script. The main changes are: 1) the text-line model for Roman script is modified to adapt to Urdu text, 2) reading order of an Urdu document is defined. The method is applied to a collection of scanned Urdu documents from various books, magazines, and newspapers. The results show high text-line detection accuracy on scanned images of Urdu prose and poetry books and magazines. The algorithm also works reasonably well on newspaper images. We also identify directions for future work which may further improve the accuracy of the system.

关键词： image analysis Text analysis Optical character recognition software Layout Algorithm design and analysis Character recognition pattern analysis Books image segmentation Noise robustness

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