Mathieu Delalandre

Building Synthetic Graphical Documents for Performance Evaluation

In this paper we present a system that allows to build synthetic graphical documents for the performance evaluation of symbol recognition systems. The key contribution of this work is the building of whole documents like drawings or maps. We exploit the layer property of graphical documents by positioning symbol sets in different ways from a same background using positioning constraints. Experiments are presented to build two kinds of test document databases : bags of symbol and architectural drawings.

Multi-Oriented and Multi-Sized Touching Character Segmentation Using Dynamic Programming

In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region at the background portion. Using Convex Hull information, we use these background information to find some initial points to segment a touching string into possible primitive segments (a primitive segment consists of a single character or a part of a character). Next these primitive segments are merged to get optimum segmentation and dynamic programming is applied using total likelihood of characters as the objective function. SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Circular ring and convex hull ring based approach has been used along with angular information of the contour pixels of the character to make the feature rotation invariant. From the experiment, we obtained encouraging results.

Generation of synthetic documents for performance evaluation of symbol recognition & spotting systems

This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results.

A general framework for the evaluation of symbol recognition methods

Performance evaluation is receiving increasing interest in graphics recognition. In this paper, we discuss some questions regarding the definition of a general framework for evaluation of symbol recognition methods. The discussion is centered on three key elements in performance evaluation: test data, evaluation metrics and protocols of evaluation. As a result of this discussion we state some general principles to be taken into account for the definition of such a framework. Finally, we describe the application of this framework to the organization of the first contest on symbol recognition in GREC’03, along with the results obtained by the participants.

Multi-oriented touching text character segmentation in graphical documents using dynamic programming

The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes.

DocMining: A Cooperative Platform for Heterogeneous Document Interpretation According to User-Defined Scenarios

The DocMining platform is aimed at providing a general framework for document interpretation. It integrates document processing units coming from different sources and communicating through the document being interpreted. A task to be performed is represented by a scenario that describes the units to be run, and each unit is associated with a contract that describes the parameters, data and results of the unit as well as the way to run it. A controller interprets the scenario and triggers each required document processing unit at its turn. Documents, scenarios and contracts are all represented in XML, to make data manipulation and communications easier.

A Contour-Based Method for Logo Detection

This paper presents a new approach for logo detection exploiting contour based features. At first stage, pre-processing, contour detection and line segmentation are done. These processes result in set of Outer Contour Strings (OCSs) describing each graphics and text parts of the documents. Then, the logo detection problem is defined as a region scoring problem. Two types of features, coarse and finer ones, are computed from each OCS. Coarse features catch graphical and domain information about OCSs, such as logo positions and aspect ratios. Finer features characterize the contour regions using a gradient based representation. Using these features, we employ regression fitting to score how likely an OCS takes part of a logo region. A final step of correction helps with the wrong segmentation cases. We present experiments done on the Tobacco-800 dataset, and compare our results with the literature. We obtain interesting results compared to the best systems.

Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview

This paper deals with the topic of performance evaluation of the symbol recognition & spotting systems. It presents an overview as a result of the work and the discussions undertaken by a working group on this subject. The paper starts by giving a general view of symbol recognition & spotting and performance evaluation. Next, the two main issues of performance evaluation are discussed: groundtruthing and performance characterization. Different problems related to both issues are addressed: groundtruthing of real documents, generation of synthetic documents, degradation models, the use of a priori knowledge, mapping of the groundtruth with the system results, and so on. Open problems arising from this overview are also discussed at the end of the paper.

Local structural analysis: A primer

The structural analysis is a processing step during which graphs are extracted from binary images. We can decompose the structural analysis into local and global approaches. The local approach decomposes the connected components, and the global approach groups them together. This paper deals especially with the local structural analysis. The local structural analysis is employed for different applications like symbol recognition, line drawing interpretation, and character recognition. We propose here a primer on the local structural analysis. First, we propose a general decomposition of the local structural analysis into four steps: object graph extraction, mathematical approximation, high-level object construction, and object graph correction. Then, we present some considerations on the method comparison and combination.

A Fast CBIR System of Old Ornamental Letter

This paper deals with the CBIR of old printed graphics (of XVI° and XVII° centuries) like the headpieces, the pictures and the ornamental letters. These graphical parts are previously segmented from digitized old books in order to constitute image databases for the historians. Today, large databases exist and involves to use automatic retrieval tools able to process large amounts of data. For this purpose, we have developed a fast retrieval system based on a Run Length Encoding (RLE) of images. We use the RLE in an image comparison algorithm using two steps: one of image centering and then a distance computation. Our centering step allows to solve the shifting problems usually met between the segmented images. We present experiments and results about our system concerning the processing time and the retrieval precision.