A System for Text Extraction in Complex-Background Document Images

Thanh-Nam Nguyen, Cong-Nhat-Nam Hoang, Thanh-Sach Le, Tuan Anh Tran

Abstract: Due to the demand of information transportation, identification, archive, the digitization of document images is increasingly concerned. Detecting text regions is the first and crucial step in End-to-End text recognition system. With the complex background document images, they are still a challenging problem due to the variety of fonts, sizes, colors of the text, and background complexity. This paper presents a system based on a Connectionist Text Proposal Network (CTPN) for extracting text regions in the document image with a complex background. This method consists of two fundamental stages: detect fine-scale text and text line extraction based on the obtained text components. We tried many-core of the feature extracting method such as VGG19, Resnet50 as well as evaluate the system's performance on many different datasets such as ICDAR2011, ICDAR2013, and a private real book cover. Besides, we also built an online visualize evaluation system to compare the results.