Abstract

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 format- ted 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 ob- ject recognition, in which we consider ob ject 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 Ro- bust 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.