PT Journal
AU Jon Almazan
   Albert Gordo
   Alicia Fornes
   Ernest Valveny
TI Segmentation-free Word Spotting with Exemplar SVMs
SO Pattern Recognition
JI PR
PY 2014
BP 3967–3978
VL 47
IS 12
DI 10.1016/j.patcog.2014.06.005
DE Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression
AB 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.
ER