Bertrand Coüasnon

DMOS, a generic document recognition method: application to table structure analysis in a general and in a specific way

AbstractWe will show in this paper one of the numerous interests of designing a generic recognition system, i.e. the possibility of producing either general or specific systems. We propose the Description and Modification of Segmentation (DMOS) method, which is made of a new grammatical language (Enhanced Position Formalism—EPF) and an associated parser able to deal with noise. From an EPF description of a kind of document structure, a new recognition system is produced by compilation. This method has been successfully used to produce recognition systems on musical scores, mathematical formulae and even tennis courts in videos. This DMOS generic method separates knowledge from program. Therefore, for a same kind of document like table structures, it is possible to define with EPF, more or less specific descriptions to produce more or less specific recognition systems. For example, we have been able to produce a general recognition system of table structures. It can recognize the hierarchical organization of a table made with rulings, whatever the number/size of column/rows and the deep of the hierarchy contents in it, as soon as the document has a not too bad quality (no missing rulings for example). We will present the way the description is done using EPF to be general enough to recognize very different table organizations. With the same DMOS generic method, we have also been able to easily define a specific recognition system of the table structure of quite damaged military forms of the 19th century. This specific description was necessary to compensate some missing informations concerning the table structure of those military forms, due to a very bad quality or hidden part of the table. This system has been successfully validated on 88,745 images, showing that this DMOS generic method can be used at an industrial level.

DMOS: a generic document recognition method, application to an automatic generator of musical scores, mathematical formulae and table structures recognition systems

Genericity in structured document recognition is a difficult challenge. We therefore propose a new generic document recognition method, called DMOS (Description and MOdification of Segmentation), that is made up of a new grammatical formalism, called EPF (Enhanced Position Formalism) and an associated parser which is able to introduce context in segmentation. We implement this method to obtain a generator of document recognition systems. This generator can automatically produce new recognition systems. It is only necessary to describe the document with an EPF grammar, which is then simply compiled. In this way, we have developed various recognition systems: one on musical scores, one on mathematical formulae and one on recursive table structures. We have also defined a specific application to damaged military forms of the 19th Century. We have been able to test the generated system on 5,000 of these military forms. This has permitted us to validate the DMOS method on a real-world application.

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.

Multiresolution cooperation makes easier document structure recognition

This paper shows the interest of imitating the perceptive vision to improve the recognition of the structure of ancient, noisy and low structured documents. The perceptive vision, that is used by human eye, consists in focusing attention on interesting elements after having detecting their presence in a global vision process. We propose a generic method in order to apply this concept to various problems and kinds of documents. Thus, we introduce the concept of cooperation between multiresolution visions into a generic method. The originality of this work is that the cooperation between resolutions is totally led by the knowledge dedicated to each kind of document. In this paper, we present this method on three kinds of documents: handwritten low structured mail documents, naturalization decree register that are archive noisy documents from the 19th century and Bangla script that requires a precise vision. This work is validated on 86,291 documents.

A real-world evaluation of a generic document recognition method applied to a military form of the 19th century

In this paper we present a real-world evaluation of DMOS, a new generic document recognition method. This method uses a new grammatical formalism (EPF) and an associated parser able to introduce context in segmentation. We have implemented this DMOS method to build an automatic generator of structured document recognition systems. We already produced three recognition systems by only changing the EPF grammar: one on musical scores, one on mathematical formulae and one on recursive table structures. We present here a specific light grammar to automatically recognize quite damaged 19th century military forms. The quality of those forms is far from perfect: table lines are not well printed, paper is so thin that there are transparency problems (the forms are two-sided) but the biggest problem comes from small paper sheets hiding part of the structure. The evaluation of this system has been made onto 5268 images and the results show that the system did not make any mistake. Moreover it can recognize the entire structure in 97.2% of the forms (the other 2.8% are automatically set apart).

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.

Using a Generic Document Recognition Method for Mathematical Formulae Recognition

We present in this paper how to apply to mathematical formulae a generic recognition method already used for musical scores, table structure and old forms recognition. We propose to use this method to recognize the structure of formulae and also to recognize some symbols made of line segments. This offers two possibilities: improving the symbol recognition when there is a lot of symbols like in mathematics; and overcoming segmentation problems we usually find in old mathematical formulae.

A way to separate knowledge from program in structured document analysis: application to optical music recognition

Optical Music Recognition is a form of document analysis for which a priori knowledge is particularly important. Musical notation is governed by a substantial set of rules, but current systems fail to use them adequately. In complex scores, existing systems cannot overcome the well-known segmentation problems of document analysis, due mainly to the high density of music information. This paper proposes a new method of recognition which uses a grammar in order to formalize the syntactic rules and represent the context. However, where objects touch, there is a discrepancy between the way the existing knowledge (grammar) will describe an object and the way it is recognized, since touching objects have to be segmented first. Following a description of the grammar, this paper shall go on to propose the use of an operator to modify the way the grammar parses the image so that the system can deal with certain touching objects (e.g. where an accidental touches a notehead).

A Symbol Classifier Able to Reject Wrong Shapes for Document Recognition Systems

We propose in this paper a new framework to develop a transparent classifier able to deal with reject notions. The generated classifier can be characterized by a strong reliability without loosing good properties in generalization. We show on a musical scores recognition system that this classifier is very well suited to develop a complete document recognition system. Indeed this classifier allows them firstly to extract known symbols in a document (text for example) and secondly to validate segmentation hypotheses. Tests had been successfully performed on musical and digit symbols databases.