Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features
Gregor Wiedemann, Gerhard Heyer · 2018
In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications.As a first step, the workflow involves scanning and Optical Character Recognition (OCR) of documents.Preservation of document contexts of single page scans is a major requirement in this context.To facilitate workflows involving very large amounts of paper scans, page stream segmentation (PSS) is the task to automatically separate a stream of scanned images into multi-page documents.In a digitization project together with a German federal archive, we developed a novel approach based on convolutional neural networks (CNN) combining image and text features to achieve optimal document separation results.Evaluation shows that our PSS architecture achieves an accuracy up to 93 % which can be regarded as a new state-of-the-art for this task.