J. Chazalon, Marçal Rusiñol, & Jean-Marc Ogier. (2015). Improving Document Matching Performance by Local Descriptor Filtering. In 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 (pp. 1216–1220).
Abstract: In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements.
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Dimosthenis Karatzas, Lluis Gomez, A.Nicolaou, Suman Ghosh, Andrew Bagdanov, Masakazu Iwamura, et al. (2015). ICDAR 2015 Competition on Robust Reading. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 1156–1160).
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Lluis Pere de las Heras, Oriol Ramos Terrades, Josep Llados, David Fernandez, & Cristina Cañero. (2015). Use case visual Bag-of-Words techniques for camera based identity document classification. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 721–725).
Abstract: Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes.
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Marçal Rusiñol, J. Chazalon, Jean-Marc Ogier, & Josep Llados. (2015). A Comparative Study of Local Detectors and Descriptors for Mobile Document Classification. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 596–600).
Abstract: In this paper we conduct a comparative study of local key-point detectors and local descriptors for the specific task of mobile document classification. A classification architecture based on direct matching of local descriptors is used as baseline for the comparative study. A set of four different key-point
detectors and four different local descriptors are tested in all the possible combinations. The experiments are conducted in a database consisting of 30 model documents acquired on 6 different backgrounds, totaling more than 36.000 test images.
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R. Bertrand, Oriol Ramos Terrades, P. Gomez-Kramer, P. Franco, & Jean-Marc Ogier. (2015). A Conditional Random Field model for font forgery detection. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 576–580).
Abstract: Nowadays, document forgery is becoming a real issue. A large amount of documents that contain critical information as payment slips, invoices or contracts, are constantly subject to fraudster manipulation because of the lack of security regarding this kind of document. Previously, a system to detect fraudulent documents based on its intrinsic features has been presented. It was especially designed to retrieve copy-move forgery and imperfection due to fraudster manipulation. However, when a set of characters is not present in the original document, copy-move forgery is not feasible. Hence, the fraudster will use a text toolbox to add or modify information in the document by imitating the font or he will cut and paste characters from another document where the font properties are similar. This often results in font type errors. Thus, a clue to detect document forgery consists of finding characters, words or sentences in a document with font properties different from their surroundings. To this end, we present in this paper an automatic forgery detection method based on document font features. Using the Conditional Random Field a measurement of probability that a character belongs to a specific font is made by comparing the character font features to a knowledge database. Then, the character is classified as a genuine or a fake one by comparing its probability to belong to a certain font type with those of the neighboring characters.
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Marçal Rusiñol, David Aldavert, Ricardo Toledo, & Josep Llados. (2015). Towards Query-by-Speech Handwritten Keyword Spotting. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 501–505).
Abstract: In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset.
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2013). New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation. In 12th International Conference on Document Analysis and Recognition (pp. 265–269).
Abstract: In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learneddictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. Evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods.
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Hongxing Gao, Marçal Rusiñol, Dimosthenis Karatzas, Josep Llados, Tomokazu Sato, Masakazu Iwamura, et al. (2013). Key-region detection for document images -applications to administrative document retrieval. In 12th International Conference on Document Analysis and Recognition (pp. 230–234).
Abstract: In this paper we argue that a key-region detector designed to take into account the special characteristics of document images can result in the detection of less and more meaningful key-regions. We propose a fast key-region detector able to capture aspects of the structural information of the document, and demonstrate its efficiency by comparing against standard detectors in an administrative document retrieval scenario. We show that using the proposed detector results to a smaller number of detected key-regions and higher performance without any drop in speed compared to standard state of the art detectors.
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Alicia Fornes, Xavier Otazu, & Josep Llados. (2013). Show through cancellation and image enhancement by multiresolution contrast processing. In 12th International Conference on Document Analysis and Recognition (pp. 200–204).
Abstract: Historical documents suffer from different types of degradation and noise such as background variation, uneven illumination or dark spots. In case of double-sided documents, another common problem is that the back side of the document usually interferes with the front side because of the transparency of the document or ink bleeding. This effect is called the show through phenomenon. Many methods are developed to solve these problems, and in the case of show-through, by scanning and matching both the front and back sides of the document. In contrast, our approach is designed to use only one side of the scanned document. We hypothesize that show-trough are low contrast components, while foreground components are high contrast ones. A Multiresolution Contrast (MC) decomposition is presented in order to estimate the contrast of features at different spatial scales. We cancel the show-through phenomenon by thresholding these low contrast components. This decomposition is also able to enhance the image removing shadowed areas by weighting spatial scales. Results show that the enhanced images improve the readability of the documents, allowing scholars both to recover unreadable words and to solve ambiguities.
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R. Bertrand, P. Gomez-Krämer, Oriol Ramos Terrades, P. Franco, & Jean-Marc Ogier. (2013). A System Based On Intrinsic Features for Fraudulent Document Detection. In 12th International Conference on Document Analysis and Recognition (pp. 106–110).
Abstract: Paper documents still represent a large amount of information supports used nowadays and may contain critical data. Even though official documents are secured with techniques such as printed patterns or artwork, paper documents suffer froma lack of security.
However, the high availability of cheap scanning and printing hardware allows non-experts to easily create fake documents. As the use of a watermarking system added during the document production step is hardly possible, solutions have to be proposed to distinguish a genuine document from a forged one.
In this paper, we present an automatic forgery detection method based on document’s intrinsic features at character level. This method is based on the one hand on outlier character detection in a discriminant feature space and on the other hand on the detection of strictly similar characters. Therefore, a feature set iscomputed for all characters. Then, based on a distance between characters of the same class.
Keywords: paper document; document analysis; fraudulent document; forgery; fake
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M. Visani, V.C.Kieu, Alicia Fornes, & N.Journet. (2013). The ICDAR 2013 Music Scores Competition: Staff Removal. In 12th International Conference on Document Analysis and Recognition (pp. 1439–1443).
Abstract: The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results.
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L. Rothacker, Marçal Rusiñol, & G.A. Fink. (2013). Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents. In 12th International Conference on Document Analysis and Recognition (pp. 1305–1309).
Abstract: 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. Therefore they can be considered as a variant of discrete HMMs allowing to model the observation of a number of features at a point in time. The discrete nature enables us to estimate a query model with only a single example of the query provided by the user. This makes our method very flexible with respect to the availability of training data. Furthermore, we are able to outperform state-of-the-art results on the George Washington dataset.
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Lluis Pere de las Heras, David Fernandez, Ernest Valveny, Josep Llados, & Gemma Sanchez. (2013). Unsupervised wall detector in architectural floor plan. In 12th International Conference on Document Analysis and Recognition (pp. 1245–1249).
Abstract: Wall detection in floor plans is a crucial step in a complete floor plan recognition system. Walls define the main structure of buildings and convey essential information for the detection of other structural elements. Nevertheless, wall segmentation is a difficult task, mainly because of the lack of a standard graphical notation. The existing approaches are restricted to small group of similar notations or require the existence of pre-annotated corpus of input images to learn each new notation. In this paper we present an automatic wall segmentation system, with the ability to handle completely different notations without the need of any annotated dataset. It only takes advantage of the general knowledge that walls are a repetitive element, naturally distributed within the plan and commonly modeled by straight parallel lines. The method has been tested on four datasets of real floor plans with different notations, and compared with the state-of-the-art. The results show its suitability for different graphical notations, achieving higher recall rates than the rest of the methods while keeping a high average precision.
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Christophe Rigaud, Dimosthenis Karatzas, Joost Van de Weijer, Jean-Christophe Burie, & Jean-Marc Ogier. (2013). An active contour model for speech balloon detection in comics. In 12th International Conference on Document Analysis and Recognition (pp. 1240–1244).
Abstract: Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent comic book understanding would enable a variety of new applications, including content-based retrieval and content retargeting. Document understanding in this domain is challenging as comics are semi-structured documents, combining semantically important graphical and textual parts. Few studies have been done in this direction. In this work we detail a novel approach for closed and non-closed speech balloon localization in scanned comic book pages, an essential step towards a fully automatic comic book understanding. The approach is compared with existing methods for closed balloon localization found in the literature and results are presented.
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Marçal Rusiñol, T.Benkhelfallah, & V. Poulain d'Andecy. (2013). Field Extraction from Administrative Documents by Incremental Structural Templates. In 12th International Conference on Document Analysis and Recognition (pp. 1100–1104).
Abstract: In this paper we present an incremental framework aimed at extracting field information from administrative document images in the context of a Digital Mail-room scenario. Given a single training sample in which the user has marked which fields have to be extracted from a particular document class, a document model representing structural relationships among words is built. This model is incrementally refined as the system processes more and more documents from the same class. A reformulation of the tf-idf statistic scheme allows to adjust the importance weights of the structural relationships among words. We report in the experimental section our results obtained with a large dataset of real invoices.
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