PT Journal
AU Jon Almazan
   Albert Gordo
   Alicia Fornes
   Ernest Valveny
TI Word Spotting and Recognition with Embedded Attributes
SO IEEE Transactions on Pattern Analysis  and Machine Intelligence
JI TPAMI
PY 2014
BP 2552
EP 2566
VL 36
IS 12
DI 10.1109/TPAMI.2014.2339814
AB 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.
ER