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Background variability modeling for statistical layout analysis

Background variability modeling for statistical layout analy...

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International Conference on pattern recognition

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 Kaiserslautern Germany Technical University of Kaiserslautern Germany

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

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

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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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A discriminative learning approach for orientation detection of Urdu document images

A discriminative learning approach for orientation detection...

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IEEE International Conference on Multi Topic

作者： Sheikh Faisal Rashid Syed Saqib Bukhari Faisal Shafait Thomas M. Breuel Image Understanding and Pattern Recognition (IUPR) Research Group Technical University of Kaiserslautern Kaiserslautern Germany German Research Center for Artificial Intelligence Kaiserslautern Germany

Orientation detection is an important preprocessing step for accurate recognition of text from document images. Many existing orientation detection techniques are based on the fact that in Roman script text ascenders occur more likely than descenders, but this approach is not applicable to document of other scripts like Urdu, Arabic, etc. In this paper, we propose a discriminative learning approach for orientation detection of Urdu documents with varying layouts and fonts. The main advantage of our approach is that it can be applied to documents of other scripts easily and accurately. Our approach is based on classification of individual connected component orientation in the document image, and then the orientation of the page image is determined via majority count. A convolutional neural network is trained as discriminative learning model for the labeled Urdu books dataset with four target orientations: 0, 90, 180 and 270 degrees. We demonstrate the effectiveness of our method on dataset of Urdu documents categorized into the layouts of book, novel and poetry. We achieved 100% orientation detection accuracy on a test set of 328 document images.

关键词： Neural networks Optical character recognition software Shape pattern recognition image recognition Books Cellular neural networks Learning Artificial intelligence Text recognition

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Improvements to Uncalibrated Feature-Based Stereo Matching for Document images by Using Text-Line Segmentation

Improvements to Uncalibrated Feature-Based Stereo Matching f...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Muhammad Zeshan Afzal Martin Kramer Syed Saqib Bukhari Faisal Shafait Thomas M. Breuel Image Understanding and Pattern Recognition Group Technical University of Kaiserslautern Kaiserslautern Germany Multimedia Analysis and Data Mining Research Group German Research Center for Artificial Intelligence Kaiserslautern Germany

Document images prove to be a difficult case for standard stereo correspondence approaches. One of the major problem is that document images are highly self-similar. Most algorithms try to tackle this problem by incorporating a global optimization scheme, which tends to be computationally expensive. In this paper, we show that incorporation of layout information into the matching paradigm, as a grouping entity for features, leads to better results in terms of robustness, efficiency, and ultimately in a better 3D model of the captured document, that can be used in various document restoration systems. This can be seen as a divide and conquer approach that partitions the search space into portions given by each grouping entity and then solves each of them independently. As a grouping entity text-lines are preferred over individual character blobs because it is easier to establish correspondences. Text-line extraction works reasonably well on stereo image pairs in the presence of perspective distortions. The proposed approach is highly efficient and matches obtained are more reliable. The claims are backed up by showing their practical applicability through experimental evaluations.

关键词： Feature extraction Robustness Three dimensional displays image segmentation Cameras Solid modeling Stereo vision

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A simple and effective approach for border noise removal from document images

A simple and effective approach for border noise removal fro...

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IEEE International Conference on Multi Topic

作者： Faisal Shafait Thomas M. Breuel Image Understanding and Pattern Recognition Research Group German Research Center for Artificial Intelligence Kaiserslautern Germany Department of Computer Science Technical University of Kaiserslautern Kaiserslautern Germany

When digitizing bound material like books or magazines, marginal noise appears along the page border. This noise consists of undesired text parts from the neighboring page and/or speckles that result from the binarization process. When a keyword based search is performed in a digitized collection, textual noise in particular poses problems since the returned search results might correspond to textual noise instead of actual contents of the page. Manually removing marginal noise for each page is not feasible in large scale digitization projects. In this paper, we present a simple and effective approach for removing both textual and non-textual noise by finding borders of noise regions using projection profile analysis. We demonstrate the effectiveness of our approach by evaluating it quantitatively on the widely used University of Washington (UW3) dataset. The results show that our approach reduces the noise ratio from 70% to 20% while retaining more than 99% of actual page contents. Comparison with state-of-the-art approaches shows that our algorithm performs comparable to them, while being simple to understand and easy to implement. We also provide an open source implementation of our method as part of the OCRopus OCR system.

关键词： Books Optical character recognition software Filters Signal to noise ratio Optical noise Character recognition Bars Cleaning image analysis pattern recognition

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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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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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Example-Based Logical Labeling of Document Title Page images

Example-Based Logical Labeling of Document Title Page Images

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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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Automated Ground Truth Data Generation for Newspaper Document images

Automated Ground Truth Data Generation for Newspaper Documen...

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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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NTIRE 2023 image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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