This paper presents a novel end-to-end system for table understanding in document images called DeepDeSRT. In particular, the contribution of DeepDeSRT is two-fold. First, it presents a deep learning-based solution for table detection in document images. Secondly, it proposes a novel deep learning-based approach for table structure recognition, i.e. identifying rows, columns, and cell positions in the detected tables. In contrast to existing rule-based methods, which rely on heuristics or additional PDF metadata (like, for example, print instructions, character bounding boxes, or line segments), the presented system is data-driven and does not need any heuristics or metadata to detect as well as to recognize tabular structures in document images. Furthermore, in contrast to most existing table detection and structure recognition methods, which are applicable only to PDFs, DeepDeSRT processes document images, which makes it equally suitable for born-digital PDFs (as they can automatically be converted into images) as well as even harder problems, e.g. scanned documents. To gauge the performance of DeepDeSRT, the system is evaluated on the publicly available ICDAR 2013 table competition dataset containing 67 documents with 238 pages overall. Evaluation results reveal that DeepDeSRT outperforms state-of-the-art methods for table detection and structure recognition and achieves F1-measures of 96.77% and 91.44% for table detection and structure recognition, respectively. Additionally, DeepDeSRT is evaluated on a closed dataset from a real use case of a major European aviation company comprising documents which are highly unlike those in ICDAR 2013. Tested on a randomly selected sample from this dataset, DeepDeSRT achieves high detection accuracy for tables which demonstrates the sound generalization capabilities of our system.

smartFIX is a product portfolio for knowledge based extraction of data from any document format. The system automatically determines the document type and extracts all relevant data for the respective business process. Data that is unreliably recognized is forwarded to a verification workplace for manual checking. In general, users have no difficulties to interpret the document data and wonder why the system needs additional input. For that reason, we implemented an explanation component that is used to justify extraction results, thus, increasing confidence of users. The component is using a semantic log making it possible to provide understandable explanations. We illustrate the benefits of that kind of technology in contrast to the current smartFIX Log Viewer by means of a preliminary user experiment.

smartFIX is a product portfolio for knowledge-based extraction of data from any document format. smartFIX automatically determines the document type and extracts all relevant data for the respective business process. Data that is uncertainly recognized is forwarded to a verification workplace for manual checking. In general, users have no difficulties to interpret the document data and wonder why the system needs additional input. For that reason, we will integrate an explanation component that will be used to justify uncertain extraction results, thus, increasing confidence of users. The component will be based on semantic technologies in general and on a semantic log in particular. The log will contain all process relevant information enabling the explanation facility to generate customized and understandable explanations. In this paper, we will discuss the benefits of that kind of technology with reference to DAS.

This paper deals with the transfer of knowledge on invoice document layout and extraction strategies, collected by users of the invoice recognition software smartFIX over several years of productive use, to other user's systems. The results of a project analyzing this 'treasure' of knowledge and putting it to use in the smartFIX system are presented. The evaluation shows that this transfer of knowledge using state-of-the-art techniques in transfer learning achieves significantly higher initial recognition rates than the unaugmented system, delivering instant economic advantages by reducing accountant personnel workload.

We describe an automated keyword extraction system which uses an eye tracker to identify those areas of a written document the reader finds of greatest interest. The keywords thus extracted are then used in the back end of an information retrieval system to help the user find other documents which contain information of interest to him.

The decomposition of a document into segments such as text regions and graphics is a significant part of the document analysis process. The basic requirement for rating and improvement of page segmentation algorithms is systematic evaluation. The approaches known from the literature have the disadvantage that manually generated reference data (zoning ground truth) are needed for the evaluation task. The effort and cost of the creation of these data are very high. This paper describes the evaluation system SEE and presents an assessment of its quality. The system requires the OCR generated text and the original text of the document in correct reading order (text ground truth) as input. No manually generated zoning ground truth is needed. The implicit structure information that is contained in the text ground truth is used for the evaluation of the automatic zoning. Therefore, an assignment of the corresponding text regions in the text ground truth and those in the OCR generated text (matches) is sought. A fault tolerant string matching algorithm underlies a method, able to tolerate OCR errors in the text. The segmentation errors are determined as a result of the evaluation of the matching. Subsequently, the edit operations which are necessary for the correction of the recognized segmentation errors are computed to estimate the correction costs. Furthermore, SEE provides a version of the OCR generated text, which is corrected from the detected page segmentation errors.

We turn to the viewpoint of users of a DAU system. Out of the view of users we sketch a picture of “Document Analysis and Understanding” (DAU), only a simple division of DAU into six sub-tasks, and consider this a model of DAU. We provide one elaborate example of a use of the model: the module design of the commercially successful DAU system smartFIX. We argue that such modeling of DAU can be of benefit to the whole field of DA