Word Spotting and Recognition with Embedded Attributes

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...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 36; no. 12; pp. 2552 - 2566
Main Authors: Almazan, Jon, Gordo, Albert, Fornes, Alicia, Valveny, Ernest
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Character recognition
Databases, Factual
Handwriting
Handwriting recognition
Hidden Markov models
Histograms
Humans
Image Processing, Computer-Assisted - methods
Image recognition
Intelligence
Machine Learning
Nearest neighbor searches
Object recognition
Pattern analysis
Pattern Recognition, Automated - methods
Regression
Strings
Subspaces
Tasks
Text recognition
scene text
attribute-based representation
handwritten text
Word image representation
word recognition
word spotting
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary: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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2014.2339814