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Author |
Andreas Fischer; Volkmar Frinken; Horst Bunke; Ching Y. Suen |
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Title |
Improving HMM-Based Keyword Spotting with Character Language Models |
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Conference Article |
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Year |
2013 |
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12th International Conference on Document Analysis and Recognition |
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506-510 |
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Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template image matching. In the lexicon-free approach pursued, only the text appearance was taken into account for recognition. In this paper, we integrate character n-gram language models into the spotting system in order to provide an additional language context. On the modern IAM database as well as the historical George Washington database, we demonstrate that character language models significantly improve the spotting performance. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.045; 605.203 |
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Admin @ si @ FFB2013 |
Serial |
2295 |
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Author |
Volkmar Frinken; Andreas Fischer; Carlos David Martinez Hinarejos |
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Title |
Handwriting Recognition in Historical Documents using Very Large Vocabularies |
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Conference Article |
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Year |
2013 |
Publication |
2nd International Workshop on Historical Document Imaging and Processing |
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67-72 |
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Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substantial problem for the selection of the recognizable vocabulary as well as the computation of the word probabilities. In this paper we propose for the transcription of historical Spanish text to keep the corpus for the n-gram limited to a sample of the target text, but expand the vocabulary with words gathered from external resources. We analyze the performance of such a transcription system with different sizes of external vocabularies and demonstrate the applicability and the significant increase in recognition accuracy of using up to 300 thousand external words. |
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Washington; USA; August 2013 |
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978-1-4503-2115-0 |
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HIP |
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DAG; 600.056; 600.045; 600.061; 602.006; 602.101 |
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Admin @ si @ FFM2013 |
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2296 |
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Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Apostolos Antonacopoulos; Josep Llados |
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Title |
An interactive appearance-based document retrieval system for historical newspapers |
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Conference Article |
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Year |
2013 |
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Proceedings of the International Conference on Computer Vision Theory and Applications |
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84-87 |
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In this paper we present a retrieval-based application aimed at assisting a user to semi-automatically segment an incoming flow of historical newspaper images by automatically detecting a particular type of pages based on their appearance. A visual descriptor is used to assess page similarity while a relevance feedback process allow refining the results iteratively. The application is tested on a large dataset of digitised historic newspapers. |
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Barcelona; February 2013 |
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VISAPP |
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DAG; 600.056; 600.045; 605.203 |
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Admin @ si @ GRK2013a |
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2290 |
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Author |
Albert Gordo |
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Title |
A Cyclic Page Layout Descriptor for Document Classification & Retrieval |
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Report |
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2009 |
Publication |
CVC Technical Report |
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128 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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CIC;DAG |
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Admin @ si @ Gor2009 |
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2387 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Embedding of Graphs with Discrete Attributes Via Label Frequencies |
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Journal Article |
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2013 |
Publication |
International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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27 |
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3 |
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1360002-1360029 |
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Keywords |
Discrete attributed graphs; graph embedding; graph classification |
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Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient. |
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DAG |
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Admin @ si @ GVB2013 |
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2305 |
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Author |
Marçal Rusiñol; R.Roset; Josep Llados; C.Montaner |
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Title |
Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation |
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Conference Article |
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2011 |
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In Proceedings of the Sixth International Workshop on Digital Technologies in Cartographic Heritage |
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CartoHerit |
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DAG |
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Admin @ si @ RRL2011b |
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1978 |
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Author |
Jon Almazan; Alicia Fornes; Ernest Valveny |
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Title |
A non-rigid appearance model for shape description and recognition |
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Journal Article |
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2012 |
Publication |
Pattern Recognition |
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PR |
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45 |
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9 |
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3105--3113 |
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Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition |
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In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach. |
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0031-3203 |
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DAG @ dag @ AFV2012 |
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1982 |
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Author |
Jon Almazan; David Fernandez; Alicia Fornes; Josep Llados; Ernest Valveny |
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Title |
A Coarse-to-Fine Approach for Handwritten Word Spotting in Large Scale Historical Documents Collection |
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Conference Article |
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2012 |
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13th International Conference on Frontiers in Handwriting Recognition |
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453-458 |
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In this paper we propose an approach for word spotting in handwritten document images. We state the problem from a focused retrieval perspective, i.e. locating instances of a query word in a large scale dataset of digitized manuscripts. We combine two approaches, namely one based on word segmentation and another one segmentation-free. The first approach uses a hashing strategy to coarsely prune word images that are unlikely to be instances of the query word. This process is fast but has a low precision due to the errors introduced in the segmentation step. The regions containing candidate words are sent to the second process based on a state of the art technique from the visual object detection field. This discriminative model represents the appearance of the query word and computes a similarity score. In this way we propose a coarse-to-fine approach achieving a compromise between efficiency and accuracy. The validation of the model is shown using a collection of old handwritten manuscripts. We appreciate a substantial improvement in terms of precision regarding the previous proposed method with a low computational cost increase. |
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978-1-4673-2262-1 |
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ICFHR |
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DAG @ dag @ AFF2012 |
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1983 |
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Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Efficient Exemplar Word Spotting |
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Conference Article |
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2012 |
Publication |
23rd British Machine Vision Conference |
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67.1- 67.11 |
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In this paper we propose an unsupervised segmentation-free method for word spotting in document images.
Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage. |
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1-901725-46-4 |
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BMVC |
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DAG |
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DAG @ dag @ AGF2012 |
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1984 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Graph Embedding in Vector Spaces by Node Attribute Statistics |
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Journal Article |
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2012 |
Publication |
Pattern Recognition |
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PR |
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45 |
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9 |
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3072-3083 |
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Structural pattern recognition; Graph embedding; Data clustering; Graph classification |
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Abstract |
Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. |
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0031-3203 |
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DAG |
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Admin @ si @ GVB2012a |
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1992 |
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