Histograms of Stroke Widths for Multi-script Text Detection and Verification in Road Scenes

Abstract

Robust text detection and recognition in arbitrarily distributed, unrestricted images is a difficult problem, e.g. when interpreting traffic panels outdoors during autonomous driving. Most previous work in text detection considers only a single script, usually Latin, and it is not able to detect text with multiple scripts. Our contribution combines an established technique -Maximum Stable Extremal Regions-with a histogram of stroke width (HSW) feature and a Support Vector Machine classifier. We combined characters into groups by raycasting and merged aligned groups into lines of text that can also be verified by using the HSW. We evaluated our detection pipeline on our own dataset of road scenes from Autobahn (German Highways), and show how the character classifier stage can be trained with one script and be successfully tested on a different one. While precision and recall match to state of the art solution. A unique characteristic of the HSW feature is that it can learn and detect multiple scripts, which we believe can yield script independence.