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Author Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny edit  doi
openurl 
  Title Segmentation-free Word Spotting with Exemplar SVMs Type (up) Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 47 Issue 12 Pages 3967–3978  
  Keywords Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression  
  Abstract 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. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. 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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  Area Expedition Conference  
  Notes DAG; 600.045; 600.056; 600.061; 602.006; 600.077 Approved no  
  Call Number Admin @ si @ AGF2014b Serial 2485  
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