articleJan 1, 2024GREEN OA
Reading digits in natural images with unsupervised feature learning
Indexed indatacite
Abstract
Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end,…
Citation impact
4,556
total citations
- FWCI
- —
- Percentile
- —
- References
- 28
Citations per year
Authors
1Topics & keywords
Topics
Keywords
- Computer science
- Artificial intelligence
- Feature (linguistics)
- Benchmark (surveying)
- Reading (process)
- Task (project management)
- Unsupervised learning
- Feature learning
UN Sustainable Development Goals
- Quality Education
No related works found for this paper.