TY  - JOUR
AU  - Jon Almazan
AU  - Albert Gordo
AU  - Alicia Fornes
AU  - Ernest Valveny
PY  - 2014//
TI  - Word Spotting and Recognition with Embedded Attributes
T2  - TPAMI
JO  - IEEE Transactions on Pattern Analysis  and Machine Intelligence
SP  - 2552
EP  - 2566
VL  - 36
IS  - 12
N2  - This article 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.
SN  - 0162-8828
UR  - http://dx.doi.org/10.1109/TPAMI.2014.2339814
N1  - DAG; 600.056; 600.045; 600.061; 602.006; 600.077
ID  - Jon Almazan2014
ER  -