Object Proposals for Text Extraction in the Wild
This work addresses scene text extraction, a domain-specific computer vision task, with an incremental improvement over existing methods.
The paper tackles the problem of scene text understanding by evaluating existing generic Object Proposals methods and proposing a new algorithm specifically designed for text, resulting in superior quality and efficiency in generating word proposals.
Object Proposals is a recent computer vision technique receiving increasing interest from the research community. Its main objective is to generate a relatively small set of bounding box proposals that are most likely to contain objects of interest. The use of Object Proposals techniques in the scene text understanding field is innovative. Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, Object Proposals techniques emerge as an alternative to the traditional text detectors. In this paper we study to what extent the existing generic Object Proposals methods may be useful for scene text understanding. Also, we propose a new Object Proposals algorithm that is specifically designed for text and compare it with other generic methods in the state of the art. Experiments show that our proposal is superior in its ability of producing good quality word proposals in an efficient way. The source code of our method is made publicly available.