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检索条件"主题词=Document image processing"

共 515 条记录，以下是41-50 订阅

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Degraded document image Binarization using Novel Background Estimation Technique 6

Degraded Document Image Binarization using Novel Background ...

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6th International Conference for Convergence in Technology (I2CT)

作者： Jindal, Harshit Kumar, Manoj Tomar, Akhil Malik, Ayush Delhi Technol Univ Dept Comp Sci Engn New Delhi India

ISBN: (纸本)9781728188768

Over the past few decades, the use of scanned historical document images has increased dramatically, especially with the emergence of online libraries and standard benchmark datasets like DIBCO. The historical documents are usually in very-poor conditions containing noises like large ink stains, bleed-through, liquid spills, uneven-background, spots, faded-ink, weak/thin text that makes the task of binarization very difficult. In this paper, we propose an effective degraded document image binarization algorithm that performs accurate text segmentation. Our method first estimates the background utilizing information from neighboring pixels and filter smoothening. The next step is background subtraction that helps in the compensation of background distortions. The document is segmented using Otsu thresholding, and then we process the image to remove the remaining noise and maximize text content using labelled connected components. Our method outperforms several existing and widely used binarization algorithms on F-measure, PSNR, DRD, and pseudo F-measure when evaluated on H-DIBCO 2016 and H-DIBCO 2018 datasets and can very effectively detect faint characters from a document image.

关键词： document image processing Degraded document image Binarization Thresholding Background estimation Noise Removal Otsu Thresholding Bilateral Filtering

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Paired image to image Translation for Strikethrough Removal from Handwritten Words 15th

Paired Image to Image Translation for Strikethrough Removal ...

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15th IAPR International Workshop on document Analysis Systems (DAS)

作者： Heil, Raphaela Vats, Ekta Hast, Anders Uppsala Univ Dept Informat Technol Div Visual Informat & Interact Uppsala Sweden Uppsala Univ Ctr Digital Humanities Uppsala Dept Arch Lib & Museums Uppsala Sweden

ISBN: (纸本)9783031065552;9783031065545

Transcribing struck-through, handwritten words, for example for the purpose of genetic criticism, can pose a challenge to both humans and machines, due to the obstructive properties of the superimposed strokes. This paper investigates the use of paired image to image translation approaches to remove strikethrough strokes from handwritten words. Four different neural network architectures are examined, ranging from a few simple convolutional layers to deeper ones, employing Dense blocks. Experimental results, obtained from one synthetic and one genuine paired strikethrough dataset, confirm that the proposed paired models outperform the CycleGAN-based state of the art, while using less than a sixth of the trainable parameters.

关键词： Strikethrough removal Paired image to image translation Handwritten words document image processing

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The Wordometer - Estimating the Number of Words Read Using document image Retrieval and Mobile Eye Tracking

The Wordometer - Estimating the Number of Words Read Using D...

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12th International Conference on document Analysis and Recognition (ICDAR)

作者： Kunze, Kai Kawaichi, Hitoshi Yoshimura, Kazuyo Kise, Koichi Osaka Prefecture Univ Sakai Osaka Japan

ISBN: (纸本)9780769549993

We introduce the Wordometer, a novel method to estimate the number of words a user reads using a mobile eye tracker and document image retrieval. We present a reading detection algorithm which works with over 91 % accuracy over 10 test subjects using 10-fold cross validation. We implement two algorithms to estimate the read words using a line break detector. A simple version gives an average error rate of 13,5 % for 9 users over 10 documents. A more sophisticated word count algorithm based on support vector regression with an RBF kernel reaches an average error rate from only 8.2 % (6.5 % if one test subject with abnormal behavior is excluded). The achieved error rates are comparable to pedometers that count our steps in our daily life. Thus, we believe the Wordometer can be used as a step counter for the information we read to make our knowledge life healthier.

关键词： document image processing image retrieval medical image processing object detection radial basis function networks regression analysis support vector machines

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An image processing Self-Training System for Ruling Line Removal Algorithms

An Image Processing Self-Training System for Ruling Line Rem...

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18th International Conference on Digital Signal processing (DSP)

作者： Prokopiou, Konstantinos Kavallieratou, Ergina Stamatatos, Efstathios Hellen Open Univ Patras Greece Univ Aegean Mitilini Greece

ISBN: (纸本)9781467358057

Ruling line removal is an important pre-processing step in document image processing. Several algorithms have been proposed for this task However, it is important to be able to take full advantage of the existing algorithms by adapting them to the specific properties of a document image collection. In this paper, a system is presented, appropriate for fine-tuning the parameters of ruling line removal algorithms or appropriately adapt them to a specific document image collection, in order to improve the results. The application of our method to an existed line removal algorithms is presented.

关键词： document image processing Ruling Line Removal Simulated Annealing

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Content-Based Lawsuits document image Retrieval 21st

Content-Based Lawsuits Document Image Retrieval

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21st EPIA Conference on Artificial Intelligence (EPIA)

作者： Freire, Daniela L. de Leon Ferreira de Carvalho, Andre Carlos Ponce Feltran, Leonardo Carneiro Nagamatsu, Lara Ayumi Ramos da Silva, Kelly Cristina Firmino, Claudemir Ferreira, Joao Eduardo Takecian, Pedro Losco Carlotti, Danilo Cavalcanti Lima, Francisco Antonio Portela, Roberto Mendes Univ Sao Paulo Sao Paulo Brazil TJSP Justice Court Sao Paulo State Sao Paulo Brazil

ISBN: (纸本)9783031164743;9783031164736

The Sao Paulo Court of Justice has the highest number of lawsuits of all courts. The lawsuits are composed of raster-scanned documents enclosed in unstructured volumes, of which some are unreadable document images. Natural Language processing techniques fail to extract from some of these documents due to the low quality of images. This article proposes a methodology to automatize the retrieval of document images from lawsuit databases based on the contents of the images. We developed a hybrid algorithm for feature extraction from document images and used a distance metric to retrieve similar images. The TJSP's database was used to validate our proposal, resulting in a system that allows finding similar images with an accuracy above eighty percent.

关键词： document image processing Content-based image recognition Deep learning techniques Convolutional neural networks models Feature extraction

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L_p-norm IDF for Large Scale image Search

L<i><sub>p</sub></i>-norm IDF for Large Scale Image Search

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26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zheng, Liang Wang, Shengjin Liu, Ziqiong Tian, Qi Tsinghua Univ Beijing 100084 Peoples R China Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (纸本)9780769549897

The Inverse document Frequency (IDF) is prevalently utilized in the Bag-of-Words based image search. The basic idea is to assign less weight to terms with high frequency, and vice versa. However, the estimation of visual word frequency is coarse and heuristic. Therefore, the effectiveness of the conventional IDF routine is marginal, and far from optimal. To tackle this problem, this paper introduces a novel IDF expression by the use of L-p-norm pooling technique. Carefully designed, the proposed IDF takes into account the term frequency, document frequency, the complexity of images, as well as the codebook information. Optimizing the IDF function towards optimal balancing between TF and pIDF weights yields the so-called L-p-norm IDF (pIDF). We show that the conventional IDF is a special case of our generalized version, and two novel IDFs, i.e. the average IDF and the max IDF, can also be derived from our formula. Further, by counting for the term-frequency in each image, the proposed L-p-norm IDF helps to alleviate the visual word burstiness phenomenon. Our method is evaluated through extensive experiments on three benchmark datasets (Oxford 5K, Paris 6K and Flickr 1M). We report a performance improvement of as large as 27.1% over the baseline approach. Moreover, since the L-p-norm IDF is computed offline, no extra computation or memory cost is introduced to the system at all.

关键词： document image processing image retrieval natural language processing

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Multiple Learned Dictionaries based Clustered Sparse Coding for the Super-Resolution of Single Text image

Multiple Learned Dictionaries based Clustered Sparse Coding ...

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12th International Conference on document Analysis and Recognition (ICDAR)

作者： Walha, Rim Drira, Fadoua Lebourgeois, Franck Garcia, Christophe Alimi, Adel M. Univ Sfax ENI Sfax REGIM BP 1173 Sfax 3038 Tunisia Univ Lyon INSA Lyon CNRS UMR5205 F-69621 Villeurbanne France

This paper addresses the problem of generating a super-resolved version of a low-resolution textual image by using Sparse Coding (SC) which suggests that image patches can be sparsely represented from a suitable dictionary. In order to enhance the learning performance and improve the reconstruction ability, we propose in this paper a multiple learned dictionaries based clustered SC approach for single text image super-resolution. For instance, a large High-Resolution/Low-Resolution (HR/LR) patch pair database is collected from a set of high quality character images and then partitioned into several clusters by performing an intelligent clustering algorithm. Two coupled HR/LR dictionaries are learned from each cluster. Based on SC principle, local patch of a LR image is represented from each LR dictionary generating multiple sparse representations of the same patch. The representation that minimizes the reconstruction error is retained and applied to generate a local HR patch from the corresponding HR dictionary. The performance of the proposed approach is evaluated and compared visually and quantitatively to other existing methods applied to text images. In addition, experimental results on character recognition illustrate that the proposed method outperforms the other methods, involved in this study, by providing better recognition rates.

关键词： character recognition dictionaries document image processing image coding image representation image resolution learning (artificial intelligence) pattern clustering text detection visual databases

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Visual Graph Analysis for Quality Assessment of Manually Labelled documents image Database 13

Visual Graph Analysis for Quality Assessment of Manually Lab...

引用

13th IAPR International Conference on document Analysis and Recognition (ICDAR)

作者： Giot, Romain Bourqui, Romain Journet, Nicholas Vialard, Anne Lab Bordelais Rech Informat UMR 5800 Domaine Univ351Cours Liberat F-33405 Talence France

ISBN: (纸本)9781479918058

The context of this paper is the labelling of a document image database in an industrial process. Our work focuses on the quality assessment of a given labelled database. In most practical cases, a database is manually labelled by an operator who has to browse sequentially the images (presented as thumbnails) until the whole database is labelled. This task is very repetitive;moreover the filing plan defining the names and number of classes is often incomplete, which leads to many labelling errors. The question is then to certify if the quality of a labelled batch is good enough to globally accept it. Our objective is to ease and speed up that evaluation that needs up to 1.5 more times than the labelling work itself. We propose an interactive tool for visualizing the data as a graph. That graph enhances similarities between documents as well as the labelling quality. We define criteria on the graph that characterize the three types of errors an operator can do: an image is mislabelled, one class should be split in more pertinent subclasses, several classes should be merged in another. This allows us to focus the operator attention on potential errors. He can then count the errors encountered while auditing the database and assess (or not) the global labelling quality.

关键词： data visualisation document image processing interactive systems visual databases data visualization global labelling quality industrial process interactive tool manually labelled document image database quality assessment visual graph analysis Complexity theory Discrete wavelet transforms image databases image edge detection Labeling Visualization visual databases Data visualisation document image processing Industrial processes Edge detection discrete wavelet transform interactive systems Complexity theory Quality assessment Visualization Packaging Manual handling Database

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Planting, Growing, and Pruning Trees: Connected Filters Applied to document image Analysis

Planting, Growing, and Pruning Trees: Connected Filters Appl...

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11th IAPR International Workshop on document Analysis Systems (DAS)

作者： Lazzara, Guillaume Geraud, Thierry Levillain, Roland EPITA Res & Dev Lab LRDE FR-94276 Le Kremlin Bicetre France

ISBN: (纸本)9781479932436

Mathematical morphology, when used in the field of document image analysis and processing, is often limited to some classical yet basic tools. The domain however features a lesser-known class of powerful operators, called connected filters. These operators present an important property: they do not shift nor create contours. Most connected filters are linked to a tree-based representation of an image's contents, where nodes represent connected components while edges express an inclusion relation. By computing attributes for each node of the tree from the corresponding connected component, then selecting nodes according to an attribute-based criterion, one can either filter or recognize objects in an image. This strategy is very intuitive, efficient, easy to implement, and actually well-suited to processing images of magazines. Examples of applications include image simplification, smart binarization, and object identification.

关键词： document image processing Mathematical Morphology Tree of Shapes image Simplification Binarization Object Identification

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Web document image retrieval system based on word spotting

Web document image retrieval system based on word spotting

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IEEE International Conference on image processing (ICIP 2006)

作者： Zagoris, K. Papamarkos, N. Chamzas, C. Democritus Univ Thrace Image Processing & Multimedia lab GR-67100 Xanthi Greece Democritus Univ Thrace Dept Elect & Comp Engn GR-67100 Xanthi Greece

ISBN: (纸本)9781424404810

Nowadays, the huge non-indexing quantities of image archives (especially document images) require the development of intelligent tools for their retrieval with convenience comparable of the texts search engines. The proposed technique addresses the document retrieval problem by a word matching procedure. It performs matching directly in the images bypassing OCR and using word-images as queries. It is constituted of two different parts: The offline and the online operation. In the offline operation, the archive of document images is examined and the results are stored in a database. The online operation consists of the web interface, the creation of the word's image and finally, the matching stage. The proposed matching process it can be described shortly as a two-threshold rating system. Finally, the proposed system has been build and it can be found in at the web address: http://***/irs2.

关键词： internet document image processing information retrieval feature extraction

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