We consider an industrial context where we deal with a stream of unlabelled documents that become available progressively over time. Based on an adaptive incremental neural gas algorithm (AING), we propose a new stream-based semi supervised active learning method (A2ING) for document classification, which is able to actively query (from a human annotator) the class-labels of documents that are most informative for learning, according to an uncertainty measure. The method maintains a model as a dynamically evolving graph topology of labelled document-representatives that we call neurons. Experiments on different real datasets show that the proposed method requires on average only 36.3% of the incoming documents to be labelled, in order to learn a model which achieves an average gain of 2.15-3.22% in precision, compared to the traditional supervised learning with fully labelled training documents.

This paper addresses the use of PGM (Probabilistic Graphical Model) for form model identification from just few items filled up by an electronic pen. Only the electronic ink is sent to the system without any indication on the form model. Two applications are made in this study: one is related to keynote form classification from its filled fields, while the second application concerns a design modelling problem for the on-line configuration of shower areas. In the former, only indications on the filled fields are sent to the system, while in the latter, the designer send strokes corresponding to the elements designed on the form model. In this application a unique form is proposed to the user to fill up the configuration of his shower area. The PGM is exploited advantageously in both cases translating precisely the relationships between corresponding elements in conditional probabilities, from individual elements up to the complete model constitution.

In our previous research, we worked on on-line form recognition by exploiting semantic constraints between fields using Bayesian networks. The semantic constraints allowed us to check the co-existence of fields filled up by hand by users. In this paper, we propose to test the use of architectural constraints for a design problem related to the modelling of shower areas. The proposed method exploits the physical dependencies between different parts of a space shower. The tests are performed on a database composed of 500 forms representing 5 models. The first results reach a recognition rate of 96.7%.

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%.

This paper shows the use of case-based reasoning (CBR) for invoice structure extraction and analysis. This method, called CBR-DIA (CBR for document invoice analysis), is adaptive and does not need any previous training. It analyses a document by retrieving and analysing similar documents or elements of documents (cases) stored in a database. The retrieval step is performed thanks to graph comparison techniques like graph probing and edit distance. The analysis step is done thanks to the information found in the nearest retrieved cases. Applied on 950 invoices, CBR-DIA reaches a recognition rate of 85.29% for documents of known classes and 76.33% for documents of unknown classes.

In this paper, an approach for adaptive region segmentation of mail-order forms for high volume application is described. Regions are first identified through a selection of their anchor points described by a constraint graph, illustrating their typographic aspects in the nodes, and their topographical relationships in the arcs. Then the identification of the actual anchor points is performed from a list of textual candidates, using the Arc Consistency Algorithm (AC4). Finally, some contextual heuristics are investigated for properly delimiting the regions. The originality of this approach lies mainly in the absence of a rigid a priori model, replaced by a simple and reliable association of anchor points. The constraint graph used for their description can be easily derived from a general logical definition of their content. Experimental results are overall encouraging and the methodology integration is under execution for commercialization.