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This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.

In this paper we present a robust system of symbol recognition using a structural approach. Our key objective here is to provide a system, equaling the statistical ones in robustness concerning the recognition, to apply next to localization. To do it we have investigated two particular structural methods: the straight line detection using Hough Transform and the vector templates matching. Experiments done on the GREC2003 database show how their combination allows to obtain high recognition results.

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