A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Historical review of OCR research and development
Document image analysis
A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video OCR: indexing digital new libraries by recognition of superimposed captions
Multimedia Systems - Special section on video libraries
ICDAR 2003 Robust Reading Competitions
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Designing human friendly human interaction proofs (HIPs)
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Recognizing objects in adversarial clutter: breaking a visual captcha
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
(Computer) vision without sight
Communications of the ACM
NEOCR: a configurable dataset for natural image text recognition
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
Large-lexicon attribute-consistent text recognition in natural images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Text extraction from scene images by character appearance and structure modeling
Computer Vision and Image Understanding
Learning visual saliency based on object's relative relationship
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Recognition of Kannada characters extracted from scene images
Proceeding of the workshop on Document Analysis and Recognition
Benchmarking recognition results on camera captured word image data sets
Proceeding of the workshop on Document Analysis and Recognition
Reading the legends of Roman Republican coins
Journal on Computing and Cultural Heritage (JOCCH)
| Hi-index | 0.02 |
We present a method for spotting words in the wild, i.e., in real images taken in unconstrained environments. Text found in the wild has a surprising range of difficulty. At one end of the spectrum, Optical Character Recognition (OCR) applied to scanned pages of well formatted printed text is one of the most successful applications of computer vision to date. At the other extreme lie visual CAPTCHAs - text that is constructed explicitly to fool computer vision algorithms. Both tasks involve recognizing text, yet one is nearly solved while the other remains extremely challenging. In this work, we argue that the appearance of words in the wild spans this range of difficulties and propose a new word recognition approach based on state-of-the-art methods from generic object recognition, in which we consider object categories to be the words themselves. We compare performance of leading OCR engines - one open source and one proprietary - with our new approach on the ICDAR Robust Reading data set and a new word spotting data set we introduce in this paper: the Street View Text data set. We show improvements of up to 16% on the data sets, demonstrating the feasibility of a new approach to a seemingly old problem.