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.
Alternative approaches seek to identify document boundaries explicitly. Such approaches are proposed in @cite_13 and @cite_9 where each individual image of the sequence is classified as either continuity of the same document (SD) or beginning of a new document (ND). For this binary classification, @cite_13 rely on textual features extracted from OCR-results and classify pages with SVM and multi-layer perceptrons (MLP). @cite_9 employ bag of visual words (BoVW) and font information obtained from OCR as features, and test performance with three binary classifiers (SVM, Random Forest, and MLP).
@cite_9 @cite_13
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In this paper, we present a method for segmentation of document page flow applied to heterogeneous real bank documents. The approach is based on the content of images and it also incorporates font based features inside the documents. Our method involves a bag of visual words (BoVW) model on the designed image based feature descriptors and a novel approach to combine the consecutive pages of a document into a single feature vector that represents the transition between these pages. The transitions here could be represented by one of the two different classes: continuity of the same document or beginning of a new document. Using the transition feature vectors, we utilize three different binary classifiers to make predictions on the relationship between consecutive pages. Our initial results demonstrate that the proposed method can exhibit promising performance for document flow segmentation at this stage. The aim of this paper is to propose a document flow supervised segmentation approach applied to real world heterogeneous documents. Our algorithm treats the flow of documents as couples of consecutive pages and studies the relationship that exists between them. At first, sets of features are extracted from the pages where we propose an approach to model the couple of pages into a single feature vector representation. This representation will be provided to a binary classifier which classifies the relationship as either segmentation or continuity. In case of segmentation, we consider that we have a complete document and the analysis of the flow continues by starting a new document. In case of continuity, the couple of pages are assimilated to the same document and the analysis continues on the flow. If there is an uncertainty on whether the relationship between the couple of pages should be classified as a continuity or segmentation, a rejection is decided and the pages analyzed until this point are considered as a "fragment". The first classification already provides good results approaching 90 on certain documents, which is high at this level of the system.