Locality Sensitive Pseudo-Code for Document Images

In this paper, we propose a novel scheme for representing character string images in the scanned document. We converted conventional multi-dimensional descriptors into pseudo-codes which have a property that: if two vectors are near in the original space then encoded pseudo-codes are 'semi equivalent with high probability. For this conversion, we combined locality sensitive hashing (LSH) indices and at the same time we also developed a new family of LSH functions that is superior to earlier ones when all vectors are constrained to lie on the surface of the unit sphere. Word spotting based on our pseudo-code becomes faster than multi-dimensional descriptor-based method while it scarcely degrades the accuracy.