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| Author | David Aldavert; Marçal Rusiñol | ||||
| Title | Synthetically generated semantic codebook for Bag-of-Visual-Words based word spotting | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 223 - 228 | ||
| Keywords | Word Spotting; Bag of Visual Words; Synthetic Codebook; Semantic Information | ||||
| Abstract | Word-spotting methods based on the Bag-ofVisual-Words framework have demonstrated a good retrieval performance even when used in a completely unsupervised manner. Although unsupervised approaches are suitable for
large document collections due to the cost of acquiring labeled data, these methods also present some drawbacks. For instance, having to train a suitable “codebook” for a certain dataset has a high computational cost. Therefore, in this paper we present a database agnostic codebook which is trained from synthetic data. The aim of the proposed approach is to generate a codebook where the only information required is the type of script used in the document. The use of synthetic data also allows to easily incorporate semantic information in the codebook generation. So, the proposed method is able to determine which set of codewords have a semantic representation of the descriptor feature space. Experimental results show that the resulting codebook attains a state-of-the-art performance while having a more compact representation. |
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| Address | Viena; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.129; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ AlR2018b | Serial | 3105 | ||
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| Author | David Aldavert; Marçal Rusiñol | ||||
| Title | Manuscript text line detection and segmentation using second-order derivatives analysis | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 293 - 298 | ||
| Keywords | text line detection; text line segmentation; text region detection; second-order derivatives | ||||
| Abstract | In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets. |
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| Address | Viena; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.129; 302.065; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ AlR2018a | Serial | 3104 | ||
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| Author | Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier | ||||
| Title | Delaunay triangulation-based features for Camera-based document image retrieval system | Type | Conference Article | ||
| Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 1-6 | ||
| Keywords | Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing | ||||
| Abstract | In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images. | ||||
| Address | Santorini; Greece; April 2016 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.061; 600.084; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ BRC2016 | Serial | 2757 | ||
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| Author | Dimosthenis Karatzas; V. Poulain d'Andecy; Marçal Rusiñol | ||||
| Title | Human-Document Interaction – a new frontier for document image analysis | Type | Conference Article | ||
| Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 369-374 | ||
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| Abstract | All indications show that paper documents will not cede in favour of their digital counterparts, but will instead be used increasingly in conjunction with digital information. An open challenge is how to seamlessly link the physical with the digital – how to continue taking advantage of the important affordances of paper, without missing out on digital functionality. This paper
presents the authors’ experience with developing systems for Human-Document Interaction based on augmented document interfaces and examines new challenges and opportunities arising for the document image analysis field in this area. The system presented combines state of the art camera-based document image analysis techniques with a range of complementary tech-nologies to offer fluid Human-Document Interaction. Both fixed and nomadic setups are discussed that have gone through user testing in real-life environments, and use cases are presented that span the spectrum from business to educational application |
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| Address | Santorini; Greece; April 2016 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.077 | Approved | no | ||
| Call Number | KPR2016 | Serial | 2756 | ||
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| Author | Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | A fine-grained approach to scene text script identification | Type | Conference Article | ||
| Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 192-197 | ||
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| Abstract | This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online. | ||||
| Address | Santorini; Grecia; April 2016 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 601.197; 600.084 | Approved | no | ||
| Call Number | Admin @ si @ GoK2016b | Serial | 2863 | ||
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| Author | Joan Mas; Alicia Fornes; Josep Llados | ||||
| Title | An Interactive Transcription System of Census Records using Word-Spotting based Information Transfer | Type | Conference Article | ||
| Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 54-59 | ||
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| Abstract | This paper presents a system to assist in the transcription of historical handwritten census records in a crowdsourcing platform. Census records have a tabular structured layout. They consist in a sequence of rows with information of homes ordered by street address. For each household snippet in the page, the list of family members is reported. The censuses are recorded in intervals of a few years and the information of individuals in each household is quite stable from a point in time to the next one. This redundancy is used to assist the transcriber, so the redundant information is transferred from the census already transcribed to the next one. Household records are aligned from one year to the next one using the knowledge of the ordering by street address. Given an already transcribed census, a query by string word spotting is applied. Thus, names from the census in time t are used as queries in the corresponding home record in time t+1. Since the search is constrained, the obtained precision-recall values are very high, with an important reduction in the transcription time. The proposed system has been tested in a real citizen-science experience where non expert users transcribe the census data of their home town. | ||||
| Address | Santorini; Greece; April 2016 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 603.053; 602.006; 600.061; 600.077; 600.097 | Approved | no | ||
| Call Number | Admin @ si @ MFL2016 | Serial | 2751 | ||
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| Author | Anders Hast; Alicia Fornes | ||||
| Title | A Segmentation-free Handwritten Word Spotting Approach by Relaxed Feature Matching | Type | Conference Article | ||
| Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 150-155 | ||
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| Abstract | The automatic recognition of historical handwritten documents is still considered challenging task. For this reason, word spotting emerges as a good alternative for making the information contained in these documents available to the user. Word spotting is defined as the task of retrieving all instances of the query word in a document collection, becoming a useful tool for information retrieval. In this paper we propose a segmentation-free word spotting approach able to deal with large document collections. Our method is inspired on feature matching algorithms that have been applied to image matching and retrieval. Since handwritten words have different shape, there is no exact transformation to be obtained. However, the sufficient degree of relaxation is achieved by using a Fourier based descriptor and an alternative approach to RANSAC called PUMA. The proposed approach is evaluated on historical marriage records, achieving promising results. | ||||
| Address | Santorini; Greece; April 2016 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 602.006; 600.061; 600.077; 600.097 | Approved | no | ||
| Call Number | HaF2016 | Serial | 2753 | ||
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| Author | Juan Ignacio Toledo; Alicia Fornes; Jordi Cucurull; Josep Llados | ||||
| Title | Election Tally Sheets Processing System | Type | Conference Article | ||
| Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 364-368 | ||
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| Abstract | In paper based elections, manual tallies at polling station level produce myriads of documents. These documents share a common form-like structure and a reduced vocabulary worldwide. On the other hand, each tally sheet is filled by a different writer and on different countries, different scripts are used. We present a complete document analysis system for electoral tally sheet processing combining state of the art techniques with a new handwriting recognition subprocess based on unsupervised feature discovery with Variational Autoencoders and sequence classification with BLSTM neural networks. The whole system is designed to be script independent and allows a fast and reliable results consolidation process with reduced operational cost. | ||||
| Address | Santorini; Greece; April 2016 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 602.006; 600.061; 601.225; 600.077; 600.097 | Approved | no | ||
| Call Number | TFC2016 | Serial | 2752 | ||
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| Author | Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero | ||||
| Title | Banknote counterfeit detection through background texture printing analysis | Type | Conference Article | ||
| Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
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| Abstract | This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark. | ||||
| Address | Rumania; May 2016 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.061; 601.269; 600.097 | Approved | no | ||
| Call Number | Admin @ si @ BRL2016 | Serial | 2950 | ||
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| Author | Christophe Rigaud; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier | ||||
| Title | Color descriptor for content-based drawing retrieval | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 267 - 271 | ||
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| Abstract | Human detection in computer vision field is an active field of research. Extending this to human-like drawings such as the main characters in comic book stories is not trivial. Comics analysis is a very recent field of research at the intersection of graphics, texts, objects and people recognition. The detection of the main comic characters is an essential step towards a fully automatic comic book understanding. This paper presents a color-based approach for comics character retrieval using content-based drawing retrieval and color palette. | ||||
| Address | Tours; Francia; April 2014 | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.056; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RKB2014 | Serial | 2479 | ||
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| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 156-160 | ||
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| Abstract | This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising. | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ DTR2014 | Serial | 2543 | ||
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| Author | Dimosthenis Karatzas; Sergi Robles; Lluis Gomez | ||||
| Title | An on-line platform for ground truthing and performance evaluation of text extraction systems | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 242 - 246 | ||
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| Abstract | This paper presents a set of on-line software tools for creating ground truth and calculating performance evaluation metrics for text extraction tasks such as localization, segmentation and recognition. The platform supports the definition of comprehensive ground truth information at different text representation levels while it offers centralised management and quality control of the ground truthing effort. It implements a range of state of the art performance evaluation algorithms and offers functionality for the definition of evaluation scenarios, on-line calculation of various performance metrics and visualisation of the results. The
presented platform, which comprises the backbone of the ICDAR 2011 (challenge 1) and 2013 (challenges 1 and 2) Robust Reading competitions, is now made available for public use. |
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| Address | Tours; Francia; April 2014 | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.056; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ KRG2014 | Serial | 2491 | ||
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| Author | P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes | ||||
| Title | A Novel Learning-free Word Spotting Approach Based on Graph Representation | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 207-211 | ||
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| Abstract | Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods. | ||||
| Address | Tours; France; April 2014 | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ WEG2014b | Serial | 2517 | ||
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| Author | David Fernandez; R.Manmatha; Josep Llados; Alicia Fornes | ||||
| Title | Sequential Word Spotting in Historical Handwritten Documents | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 101 - 105 | ||
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| Abstract | In this work we present a handwritten word spotting approach that takes advantage of the a priori known order of appearance of the query words. Given an ordered sequence of query word instances, the proposed approach performs a
sequence alignment with the words in the target collection. Although the alignment is quite sparse, i.e. the number of words in the database is higher than the query set, the improvement in the overall performance is sensitively higher than isolated word spotting. As application dataset, we use a collection of handwritten marriage licenses taking advantage of the ordered index pages of family names. |
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| Address | Tours; Francia; April 2014 | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.061; 600.056; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ FML2014 | Serial | 2462 | ||
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| Author | Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier | ||||
| Title | Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 181 - 185 | ||
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| Abstract | Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to re-capture later a document with temporary availability. In this context, out-of-focus blur is major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given. | ||||
| Address | Tours; France; April 2014 | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 601.223; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RCO2014a | Serial | 2545 | ||
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