Publication
Layout Error Correction using Deep Neural Networks
Srie Raam Mohan; Syed Saqib Bukhari; Andreas Dengel
In: DAS. IAPR International Workshop on Document Analysis Systems (DAS-2018), April 24-27, IEEE, 2018.
Abstract
Layout Analysis, mainly including binarization and
page/line segmentation, 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historical document images, which contain noise, distortions and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 layout error
correction technique which include a VGG Net to classify non-
textline and adversarial network approach to obtain the layout
bounding mask. The presented layout error correction technique
are applied to a collection of 15th century Latin documents, which
achieved more than 75% accuracy for segmentation techniques.