Layout Error Correction Using Deep Neural Networks

Abstract

Layout analysis, mainly including binarization and text-line extraction, is one of the most important performance determining steps of an OCR system for complex medieval historical document images, which contain noise, distortions and irregular layouts. In this paper, we present a novel text-line error correction technique which include a VGG Net to classify non-text-line and adversarial network approach to obtain the layout bounding mask. The presented text-line error correction technique are applied to a collection of 15th century Latin documents, which achieved more than 75% accuracy for segmentation techniques.