Automatic data labeling for document image segmentation using deep neural networks

Abstract

The article proposes a new method for automatic data annotation for solving the problem of document image segmentation using deep object detection neural networks. The format of marked PDF files is considered as the initial data for markup. The peculiarity of this format is that it includes hidden marks that describe the logical and physical structure of the document. To extract them, a tool has been developed that simulates the operation of a stack-based printing machine according to the PDF format specification. For each page of the document, an image and annotation are generated in PASCAL VOC format. The classes and coordinates of the bounding boxes are calculated during the interpretation of the labeled PDF file based on the labels. To test the method, a collection of marked up PDF files was formed from which images of document pages and annotations for three segmentation classes (text, table, figure) were automatically obtained. Based on these data, a neural network of the EfficientDet D2 architecture was trained. The model was tested on manually labeled data from the same domain, which confirmed the effectiveness of using automatically generated data for solving applied problems.