End-to-End Text Recognition with Convolutional Neural Networks
Abstract/Contents
- Abstract
- Full end-to-end text recognition in natural images is a challenging problem that has recently received much attention in computer vision and machine learning. Traditional systems in this area have relied on elaborate models that incorporate carefully hand-engineered features or large amounts of prior knowledge. In this thesis, I describe an alternative approach that combines the representational power of large, multilayer neural networks with recent developments in unsupervised feature learning. This particular approach enables us to train highly accurate text detection and character recognition modules. Because of the high degree of accuracy and robustness of these detection and recognition modules, it becomes possible to integrate them into a full end-to-end, lexicon-driven, scene text recognition system using only simple off-the-shelf techniques. In doing so, we demonstrate state-of-the-art performance on standard benchmarks in both cropped-word recognition as well as full end-to-end text recognition.
Description
| Type of resource | text |
|---|---|
| Date created | 2012-05 |
Creators/Contributors
| Author | Wu, David J. |
|---|---|
| Advisor | Ng, Andrew Y. |
| Department | Stanford University. Department of Computer Science. |
Subjects
| Subject | Neural networks (Computer science) |
|---|---|
| Subject | Text processing (Computer science) |
| Genre | Thesis |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Preferred citation
- Preferred Citation
- Wu, David J. (2012) End-to-End Text Recognition with Convolutional Neural Networks. Stanford Digital Repository. Available at http://purl.stanford.edu/bj798mj3298
Collection
Undergraduate Theses, School of Engineering
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- engreference@stanford.edu
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