Aurélie Lemaitre

Interest of syntactic knowledge for on-line flowchart recognition

In this paper, we address the problem of segmentation and recognition of on-line a posteriori flowcharts. Flowcharts are bi-dimensional documents, in the sense that the order of writing is not defined. Some statistical approaches have been proposed in the literature to label and segment the flowcharts. However, as they are very well structured documents, we propose to introduce some structural and syntactic knowledge on flowcharts to improve their recognition. For this purpose, we have used an existing grammatical off-line method with on-line a posteriori signal. We apply this work on a freely available database. The results demonstrate the interest of structural knowledge on the context to improve the recognition.

Fusion of Statistical and Structural Information for Flowchart Recognition

A critical step of on-line handwritten diagram recognition is the segmentation between text and symbols. It is still an open problem in several approaches of the literature. However, for a human operator, text/symbol segmentation is an easy task and does not even need understanding diagram semantics. It is done thanks to the use of both structural knowledge and statistical analysis. A human operator knows what is a symbol and how to distinguish a good symbol from a bad one in a list of candidates. We propose to reproduce this perceptive mechanism by introducing some statistical information inside of a grammatical method for document structure recognition, in order to combine both structural an statistical knowledge. This approach is applied to flowchart recognition on a freely available database. The results demonstrate the interest of combining statistical and structural information for perceptive vision in diagram recognition.

A perceptive method for handwritten text segmentation

This paper presents a new method to address the problem of handwritten text segmentation into text lines and words. Thus, we propose a method based on the cooperation among points of view that enables the localization of the text lines in a low resolution image, and then to associate the pixels at a higher level of resolution. Thanks to the combination of levels of vision, we can detect overlapping characters and re-segment the connected components during the analysis. Then, we propose a segmentation of lines into words based on the cooperation among digital data and symbolic knowledge. The digital data are obtained from distances inside a Delaunay graph, which gives a precise distance between connected components, at the pixel level. We introduce structural rules in order to take into account some generic knowledge about the organization of a text page. This cooperation among information gives a bigger power of expression and ensures the global coherence of the recognition. We validate this work using the metrics and the database proposed for the segmentation contest of ICDAR 2009. Thus, we show that our method obtains very interesting results, compared to the other methods of the literature. More precisely, we are able to deal with slope and curvature, overlapping text lines and varied kinds of writings, which are the main difficulties met by the other methods.

Competitive Hybrid Exploration for Off-Line Sketches Structure Recognition

We work on new strategies of exploration for interpretation of off-line sketches. A first approach (call IMISketch) was based on a competitive breadth-first exploration of the analysis tree allowing to evaluate simultaneously several possible hypotheses of recognition in a dynamic local context of document. A great advantage of this strategy is to be able to solicit the user during the decision process to avoid error accumulation in the analysis step. IMISketch strategy is very interesting but it can lead combinatory problems when addressing complex sketches. In this paper, we propose a new hybrid strategy for exploration. The recognition process alternates between a breadth-first and depth-first exploration. The strategy is totally driven by the grammatical description of the document. The paper demonstrates the interest of this new hybrid strategy method on handwritten 2D architectural floor plans containing walls, opening and furnitures.

Interactive Competitive Breadth-First Exploration for Sketch Interpretation

In this paper, we present a new generic method for an interactive interpretation of sketches. This method is based on a competitive breadth-first exploration of the analysis tree. As opposed to well known structural approaches, this method allows to evaluate simultaneously several possible hypotheses of recognition in a dynamic local context of document. At each step of the analysis, the decision process selects the best hypotheses. If it detects an ambiguity, it will solicit the user to select the right hypothesis. In fact, the user participation has a great impact to avoid error accumulation during the analysis step and overcomes the combinatory due to the sketch complexity. This paper demonstrates this interactive method on 2D architectural floor plans.

Incremental learning for interactive sketch recognition

In this paper, we present the integration of a classifier, based on an incremental learning method, in an interactive sketch analyzer. The classifier recognizes the symbol with a degree of confidence. Sometimes the analyzer considers that the response is insufficient to make the right decision. The decision process then solicits the user to explicitly validate the right decision. The user associates the symbol to an existing class, to a newly created class or ignores this recognition. The classifier learns during the interpretation phase. We can thus have a method for auto-evolutionary interpretation of sketches. In fact, the user participation has a great impact to avoid error accumulation during the analysis. This paper demonstrates this integration in an interactive method based on a competitive breadth-first exploration of the analysis tree for interpreting the 2D architectural floor plans.

Interest of perceptive vision for document structure analysis

This work addresses the problem of document image analysis, and more particularly the topic of document structure recognition in old, damaged and handwritten document. The goal of this paper is to present the interest of the human perceptive vision for document analysis. We focus on two aspects of the model of perceptive vision: the perceptive cycle and the visual attention. We present the key elements of the perceptive vision that can be used for document analysis. Thus, we introduce the perceptive vision in an existing method for document structure recognition, which enable both to show how we used the properties of the perceptive vision and to compare the results obtained with and without perceptive vision. We apply our method for the analysis of several kinds of documents (archive registers, old newspapers, incoming mails . . . ) and show that the perceptive vision significantly improves their recognition. Moreover, the use of the perceptive vision simplifies the description of complex documents. At last, the running time is often reduced.

Boosting bonsai trees for handwritten/printed text discrimination

Boosting over decision-stumps proved its efficiency in Natural Language Processing essentially with symbolic features, and its good properties (fast, few and not critical parameters, not sensitive to over-fitting) could be of great interest in the numeric world of pixel images. In this article we investigated the use of boosting over small decision trees, in image classification processing, for the discrimination of handwritten/printed text. Then, we conducted experiments to compare it to usual SVM-based classification revealing convincing results with very close performance, but with faster predictions and behaving far less as a black-box. Those promising results tend to make use of this classifier in more complex recognition tasks like multiclass problems.

Optimization Analysis Based on a Breadth-First Exploration for a Structural Approach of Sketches Interpretation

In this paper, we present an optimized approach, based on a competitive breadth-first exploration of the analysis tree, for an interactive interpretation of off-line sketch. The competitive breadth-first exploration of the analysis tree, allows to compare several hypotheses of interpretation to deal with confusion. Unfortunately, in practice these methods are rarely used because they often induce a large combinatory. This paper presents an optimization strategy to minimize the combinatory. The aim is to demonstrate the relevance of a competitive breadth-first exploration in off-line document analysis, in particular when the approach is interactive, ie the user is involved into the loop analysis. This paper demonstrates this optimized interactive analysis method on off-line handwritten 2D architectural floor plans.

Iterative Analysis of Pages in Document Collections for Efficient User Interaction

The analysis of sets of degraded documents, like historical ones, is error-prone and requires human help to achieve acceptable quality levels. However, human interaction raises 3 main issues when processing important amounts of pages: none of the user or the system should wait for work, information provided by a human operator should not be restricted to local isolated corrections, but rather produce durable changes in the system, the ability to interact with a human operator should not increase the complexity of document models nor duplicate them between analysis and human interaction processes. To solve those issues, we propose an iterative approach, based on a special mechanism called visual memory, to reintegrate external information during page analysis. So as to demonstrate the interest for existing systems, we explain how we adapted a (rule-based) page analysis tool to enable, in this iterative approach, a delayed interaction with a human operator based on an adaptation of error recovery principles for compilers and the well-known exception handling mechanism. We validated our iterative approach on sales registers from the 18th century.