Table characteristics vary widely. Consequently, a great variety of computational approaches have been applied to table recognition. In this survey, the table recognition literature is presented as an interaction of table models, observations, transformations and inferences. A table model defines the physical and logical structure of tables; the model is used to detect tables, and to analyze and decompose the detected tables. Observations perform feature measurements and data lookup, transformations alter or restructure data, and inferences generate and test hypotheses. This presentation clarifies the decisions that are made by a table recognizer, and the assumptions and inferencing techniques that underlie these decisions.

In this paper, we discuss how the focus in document analysis, generally speaking, and in graphics recognition more specifically, has moved from re-engineering problems to indexing and information retrieval. After a review of ongoing work on these topics, we propose some challenges for the years to come.

Abstract. Although the internet offers a wide-spread platform for information interchange, day-to-day work in large companies still means the processing of tens of thousands of printed documents every day. This paper presents the system smartFIX which is a document analysis and understanding system deve loped by the DFKI spin-off INSIDERS. It permits the processing of documents ranging from fixed format forms to unstructured letters of any format. Apart from the architecture, the main components and system characteristics, we also show some results when applying smartFIX to medical bills and prescriptions. 1

Table recognition algorithms may be described by models of table location and struc-ture, and decisions made relative to these models. These algorithms are usually defined informally as a sequence of decisions with supporting data observations and transformations. In this investigation, we formalize these algorithms as strategies in an imitation game, where the goal of the game is to match table interpretations from a chosen procedure as closely as possible. The chosen procedure may be a person or persons producing ‘ground truth, ’ or an algorithm. To describe table recognition strategies we have defined the Recognition Strat-egy Language (RSL). RSL is a simple functional language for describing strategies as sequences of abstract decision types whose results are determined by any suit-able decision method. RSL defines and maintains interpretation trees, a simple data structure for describing recognition results. For each interpretation in an interpreta-tion tree, we annotate hypothesis histories which capture the creation, revision, and rejection of individual hypotheses, such as the logical type and structure of regions. We present a proof-of-concept using two strategies from the literature. We demon-strate how RSL allows strategies to be specified at the level of decisions rather than ii algorithms, and we compare results of our strategy implementations using new tech-niques. In particular, we introduce historical recall and precision metrics. Con-ventional recall and precision characterize hypotheses accepted after a strategy has finished. Historical recall and precision provide additional information by describing all generated hypotheses, including any rejected in the final result. iii

Abstract. An approach is presented to guide the benchmarking of invoice analysis systems, a specific, applied subclass of document analysis systems. The state of the art of benchmarking of document analysis systems is presented, based on the processing levels: Document Page Segmentation, Text Recognition, Document Classification, and Information Extraction. The restriction to invoices enables and requires a more purposeful, i.e. detailed, targetting of the benchmarking procedures (acquisition of ground truth data, system runs, comparison of data, condensation into meaningful numbers). Therefore the processing of invoices is dissected. The involved data structures are elicited and presented. These are provided, being the building blocks of the actual benchmarking of invoice analysis systems. 1

This paper presents an end-to-end administrative document analysis system. This system uses case-based reasoning in order to process documents from known and unknown classes. For each document, the system retrieves the nearest processing experience in order to analyze and interpret the current document. When a complete analysis is done, this document needs to be added to the document database. This requires an incremental learning process in order to take into account every new information, without losing the previous learnt ones. For this purpose, we proposed an improved version of an already existing neural network called Incremental Growing Neural Gas. Applied on documents learning and classification, this neural network reaches a recognition rate of 97.63%. 1.