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Marçal Rusiñol, T.Benkhelfallah and V. Poulain d'Andecy. 2013. Field Extraction from Administrative Documents by Incremental Structural Templates. 12th International Conference on Document Analysis and Recognition.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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Albert Gordo, Marçal Rusiñol, Dimosthenis Karatzas and Andrew Bagdanov. 2013. Document Classification and Page Stream Segmentation for Digital Mailroom Applications. 12th International Conference on Document Analysis and Recognition.621–625.
Abstract: In this paper we present a method for the segmentation of continuous page streams into multipage documents and the simultaneous classification of the resulting documents. We first present an approach to combine the multiple pages of a document into a single feature vector that represents the whole document. Despite its simplicity and low computational cost, the proposed representation yields results comparable to more complex methods in multipage document classification tasks. We then exploit this representation in the context of page stream segmentation. The most plausible segmentation of a page stream into a sequence of multipage documents is obtained by optimizing a statistical model that represents the probability of each segmented multipage document belonging to a particular class. Experimental results are reported on a large sample of real administrative multipage documents.
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L. Rothacker, Marçal Rusiñol and G.A. Fink. 2013. Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents. 12th International Conference on Document Analysis and Recognition.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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Ernest Valveny, Oriol Ramos Terrades, Joan Mas and Marçal Rusiñol. 2013. Interactive Document Retrieval and Classification. In Angel Sappa and Jordi Vitria, eds. Multimodal Interaction in Image and Video Applications. Springer Berlin Heidelberg, 17–30.
Abstract: In this chapter we describe a system for document retrieval and classification following the interactive-predictive framework. In particular, the system addresses two different scenarios of document analysis: document classification based on visual appearance and logo detection. These two classical problems of document analysis are formulated following the interactive-predictive model, taking the user interaction into account to make easier the process of annotating and labelling the documents. A system implementing this model in a real scenario is presented and analyzed. This system also takes advantage of active learning techniques to speed up the task of labelling the documents.
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Thanh Ha Do, Salvatore Tabbone and Oriol Ramos Terrades. 2013. New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation. 12th International Conference on Document Analysis and Recognition.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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Jon Almazan, Albert Gordo, Alicia Fornes and Ernest Valveny. 2013. Handwritten Word Spotting with Corrected Attributes. 15th IEEE International Conference on Computer Vision.1017–1024.
Abstract: We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results.
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Francisco Alvaro, Francisco Cruz, Joan Andreu Sanchez, Oriol Ramos Terrades and Jose Miguel Bemedi. 2013. Page Segmentation of Structured Documents Using 2D Stochastic Context-Free Grammars. 6th Iberian Conference on Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 133–140. (LNCS.)
Abstract: In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for page segmentation of structured documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the page segmentation is obtained as the most likely hypothesis according to a grammar. This approach is compared to Conditional Random Fields and results show significant improvements in several cases. Furthermore, grammars provide a detailed segmentation that allowed a semantic evaluation which also validates this model.
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Francisco Cruz and Oriol Ramos Terrades. 2013. Handwritten Line Detection via an EM Algorithm. 12th International Conference on Document Analysis and Recognition.718–722.
Abstract: In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results.
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Thanh Ha Do, Salvatore Tabbone and Oriol Ramos Terrades. 2013. Document noise removal using sparse representations over learned dictionary. Symposium on Document engineering.161–168.
Abstract: best paper award
In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental
results on several datasets demonstrate the robustness of our method compared with the state-of-the-art.
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Jon Almazan, Alicia Fornes and Ernest Valveny. 2013. A Deformable HOG-based Shape Descriptor. 12th International Conference on Document Analysis and Recognition.1022–1026.
Abstract: In this paper we deal with the problem of recognizing handwritten shapes. We present a new deformable feature extraction method that adapts to the shape to be described, dealing in this way with the variability introduced in the handwriting domain. It consists in a selection of the regions that best define the shape to be described, followed by the computation of histograms of oriented gradients-based features over these points. Our results significantly outperform other descriptors in the literature for the task of hand-drawn shape recognition and handwritten word retrieval
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