Segmentation-free word spotting with exemplar SVMs
| Autor: | Ernest Valveny, Albert Gordo, Alicia Fornés, Jon Almazan |
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| Rok vydání: | 2014 |
| Předmět: |
Computer science
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Spotting Machine learning computer.software_genre Support vector machine Query expansion ComputingMethodologies_PATTERNRECOGNITION Discriminative model Artificial Intelligence Sliding window protocol Signal Processing Unsupervised learning Segmentation Computer Vision and Pattern Recognition Artificial intelligence business computer Software Word (computer architecture) |
| Zdroj: | Pattern Recognition. 47:3967-3978 |
| ISSN: | 0031-3203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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