Text localization in born-digital images of advertisements

Localizing text in images is an important step in a number of applications and fundamental for optical character recognition. While born-digital text localization might look similar to other complex tasks in this field, it has certain distinct characteristics. Our novel approach combines individual strengths of the commonly used methods: stroke width transform and extremal regions and combines them with a method based on edge-based morphologically growing. We present a parameterfree method with high flexibility to varying text sizes and colorful image elements. We evaluate our method on a novel image database of different retail prospects, containing textual product information. Our results show a higher f-score than competitive methods on that particular task.