Semantic Text Detection in Born-Digital Images via Fully Convolutional Networks

Abstract

Traditional layout analysis methods cannot be easily adapted to born-digital images which carry properties from both regular document images and natural scene images. One layout approach for analyzing born-digital images is to separate the text layer from the graphics layer before further analyzing any of them. In this paper, we propose a method for detecting text regions in such images by casting the detection problem as a semantic object segmentation problem. The text classification is done in a holistic approach using fully convolutional networks where the full image is fed as input to the network and the output is a pixel heat map of the same input image size. This solves the problem of low resolution images, and the variability of text scale within one image. It also eliminates the need for finding interest points, candidate text locations or low level components. The experimental evaluation of our method on the ICDAR 2013 dataset shows that our method outperforms stateof-the-art methods. The detected text regions also allow flexibility to later apply methods for finding text components at character, word or textline levels in different orientations.

SlideImages: A Dataset for Educational Image Classification

“…Research on text extraction such as Morris et al [4] and Nayef & Logier [5] has shown that even noiseless born-digital images are sometimes better analyzed with neural nets than with handcrafted features and heuristics. Born-digital and educational images need further benchmarks related to challenging information retrieval tasks in order to test the generalization of methods for those tasks.…”