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.
Although with @cite_4 there is already one study employing neural network technology not only for DIC but also for PSS, their approach was not applicable to our project for two reasons. First, as mentioned earlier, they perform PSS only indirectly based on changing class labels of consecutive pages. Since we only have 17 document categories and a majority of them belong to one category ("letter"), we need to perform direct separation of the page stream by classifying each page into either SD or ND. Second, quality and layout of our data is extremely heterogeneous due to the long time period of document creation. We expect a lowered performance by solely relying on visual features for separation. Therefore, taking the previous work of @cite_4 as a starting point, we propose our approach for direct PSS as a binary classification task combining textual features and visual features using deep neural networks. We compare this architecture against a baseline comprising an SVM classifier solely relying on textual features.
@cite_4
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In this manuscript we propose a novel method for jointly page stream segmentation and multi-page document classification.The end goal is to classify a stream of pages as belonging to different classes of documents. We take advantage of the recent state-of-the-art results achieved using deep architectures in related fields such as document image classification, and we adopt similar models to obtain satisfying classification accuracies and a low computational complexity. Our contribution is twofold: first, the extraction of visual features from the processed documents is automatically performed by the chosen Convolutional Neural Network; second, the predictions of the same network are further refined using an additional deep model which processes them in a classic sliding-window manner to help finding and solving classification errors committed by the first network. The proposed pipeline has been evaluated on a publicly available dataset composed of more than half a million multi-page documents collected by an on-line loan comparison company, showing excellent results and high efficiency.