In this article, a system to detect rooms in architectural floor plan images is described. We first present a primitive extraction algorithm for line detection. It is based on an original coupling of classical Hough transform with image vectorization in order to perform robust and efficient line detection. We show how the lines that satisfy some graphical arrangements are combined into walls. We also present the way we detect some door hypothesis thanks to the extraction of arcs. Walls and door hypothesis are then used by our room segmentation strategy; it consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines between regions can also be rough. We take advantage of knowledge associated to architectural floor plans in order to obtain mostly rectangular rooms. Qualitative and quantitative evaluations performed on a corpus of real documents show promising results.

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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.

This paper is based on a research project aiming at improving learning arithmetic operations at school using penbased tablets. Given an arithmetic operation instruction, the goal is to analyze a child's handwritten answer. This comes down to find if any mistakes are made and their nature. An adapted representation and similarity search are needed for this analysis. In this paper, we propose to use a valued graph representation for handwritten arithmetical operations. To produce the analysis, we compute a similarity search with the corresponding expected answer using Graph Edit Distance (GED). To make up for the uncertainty of the noisy handwritten input recognition, we produce several segmented graph hypotheses for a single answer. Using the GED, we are able to correlate each hypothesis to the instruction graph. It enables to highlight multiple kinds of mistakes a child can make. The GED computation being a NPcomplete problem, we propose to use sub-graph isomorphism: we partially match the instruction on each hypothesis in polynomial time to cut part of the tree search. Experiments were conducted on an in-house dataset composed of 400 handwritten arithmetical additions written by children on pen-based tablet. The time required for the GED computation is evaluated. We are able to match the complete operation in reasonable time on larger graphs while finding most of the time the best corresponding hypothesis.

This paper presents a fuzzy visibility graph representation for handwritten mathematical expressions (HME) computed over segmented symbols using learned fuzzy landscape (FL) models. The learned FL models define the relative positioning of a pair of symbols using both their morphology, their typology and their context. A Random Forest Classifier uses this relative positioning to qualify relationships between symbols. The valued fuzzy visibility graph with the FL membership is produced from this classifier's output. This graph offers an explicit representation of the HME bi-dimensional structure which is then parsed with a set of rules to produce the recognized HME. We evaluate the performance of this system on the task of HME structure recognition using provided segmented symbols with experimental results on both CROHME 2014 and 2016 datasets. We obtain results up to par with the state-of-theart thus proving that our fuzzy visibility graphs are a strong representation for mathematical expression parsing.

In this paper, we present a new method for on-line Chinese character recognition that relies on an explicit description of characters structure. Contrary to most of known structural approaches, this model can describe characters written in a fluent style, thanks to a flexible fuzzy modeling of shapes and positioning of their structural components (primitives and radicals). We designed a process for incremental training of the models cooperated with automatic structural labeling for minimizing the required manual task in model design. First experiments show that the method is able to recognize non-regularly written characters and has a convincing generalization ability.

In this paper, we propose an original hybrid statistical-structural method for on-line Chinese character recognition. We model characters thanks to fuzzy inference rules combining morphological and contextual information formalized in a homogeneous way. For that purpose, we define a set of primitives modeling all the stroke classes that can be found in handwritten Chinese characters. Thus, each analyzed stroke can be classified as primitive without any segmentation process. Inference rules are built from the coupling of a priori information about the primitives constituting the characters and automatic modeling of their relative positioning. The fuzzy inference system aggregates these rules for decision making. First experiments validate this method with a recognition rate of 97.5% on a subset of Chinese characters.

In this paper, a new approach for on-line handwritten structured document interpretation is presented. It aims at interpreting the strokes progressively. The major component of our approach is a flexible formalism for the recognition of the document elements. Its originality is the modelling of the document global structure. The system then drives dedicated recognizers and looks only for the likely symbols depending on the document structural context in which that element is located. Moreover, the formalism defines a "canonical on-line signal form" of the recognizer entries to facilitate the interpretation process which is then more robust and more efficient. To highlight its genericity, this approach has been used to design two pen-based prototypes, for musical score editing and for graph editing.