FMN: Two-stage web image fine-grained text detection method based on full convolutional network

Abstract

In the Internet, there are large-scale web images with different image sizes and different resolutions. In order to accurately and efficiently identify fine-grained texts in web images, this paper uses FCN to support the ability to semantically segment images, and treats text and background as different detection targets. A two-stage web image fine-grained text detection method FMN is proposed. For the problem that the output result of the whole convolutional network is not fine enough, the MSER+NMS algorithm is used to extract the fine-grained features of the FCN output, and the ellipse fitting method is introduced to detect the text. Features such as character tilt and morphological differences ultimately result in text detection of the image. Compared with the existing methods, the FMN algorithm proposed in this paper has achieved certain improvement in detail resolution and precision.