CNN Based Page Object Detection in Document Images

Xiaohan Yi, Liangcai Gao, Yuan Liao, Xiaode Zhang, Runtao Liu, Zhuoren Jiang

Abstract: This electronic document is a "live" template. The various components of your paper [title, text, heads, etc.] are Abstract-Object detection in natural scenes has been widely researched in the past decade, and many deep learning based methods have achieved good performance on this task. This paper focuses on how to transfer and refine those object detection approaches from natural scene images to documents images, and proposes a deep learning-based page object (e.g., tables, formulae, figures) detection method. On the basis of traditional Convolutional Neural Network (CNN) based object detection methods, we redesign the region proposal method, the training strategy, the network structure and replace the Non-Maximum Suppression (NMS) with a dynamic programming algorithm. The experimental results show that it is essential to adjust some modules of the natural scene object detection approaches in order to better process the document images. The proposed method also achieved better performance compared with existing page object detection methods.