Shape Tracking Methods for Graphics Recognition

Shape Tracking Methods for Graphics Recognition: a Short Survey Mathieu Delalandre1 and Tony Pridmore2 and Eric Trupin1 and Jean-Marc Ogier3 1

PSI Laboratory, University of Rouen, Mont-Saint-Aignan city, France [email protected]

2

SCSIT, University of Nottingham, Nottingham city, United Kingdom [email protected]

3

L3i Laboratory, University of La Rochelle, La Rochelle city, France [email protected]

Graphics recognition is a stage of document image interpretation. It is used for different purposes like : line drawing interpretation, symbol recognition, handwriting recognition (especially asian), and so on. It is a wellknown problem and several commercial application exist. A graphics recognition process can be decomposed in two main steps : the extraction of graphical primitives from images, and the recognition these ones. This paper deals with the first step. This one extracts graphical primitives which compose the graphical shapes of document images [9] [7]. This step employs many methods in order to extract different graphical primitive types : pixel, vectorial (vector, arc, and curve), region (subset of connected pixels on image), and symbol (a symbolic label). These graphical primitives are next organized into higher-level structures like graph. The well known methods are based on skeletonization and contouring. Theses ones are considered as two steps methods [7]. They use intermediate image representations (skeleton and contours images) before the extraction of graphical primitives. The shape tracking methods are another way to extract graphical primitives. They are based on the use of elements in order to track the shapes. The graphical primitives are then extracted using one step (without intermediate representation). The first works on these methods have been introduced by [1]. Today several works exist : [5], [4], [2], [3], [10], and [8]. Some surveys on graphics recognition develop too some parts on these methods [9] [7]. Several of these methods allow to approximate the medial axis of shapes with vectorial data (vector and arc). So they are known too in the literature as direct vectorization methods. In spite of these works, these methods are few used and known [9] [7]. In this paper we present a “short” survey of these methods. We have based this survey on an original taxonomy of these methods that we present in table (1). From our point of view, the shape tracking methods can be decomposed in two tracking types : the line tracking and the junction tracking. Next the tracking process depends of used tracking elements. These ones are of two types : pixel element and surface element. The figure (1) present some examples of line and junction tracking based on pixel and surface elements. We develop these tracking types in the paper’s follow-up. We propose next some perspectives about these methods.

Line Tracking Junction Tracking

Pixel [5] [4] [3] [8] [3] [8]

Surface [2] [10] [5] [4] [2] [10]

Table 1: Taxonomy of Shape Tracking Methods The pixel based tracking uses the contours in order to control the tracking of a pixel element. Concerning the lines two types of tracking are used : orthogonal [3] and linear [5] [4] [8]. The first one [3] bounces on contours and tracks the shape in an orthogonal way (figure (1) (a)). Two tracking are then used : vertical and orthogonal. The tracking results are next merged in order to approximate the medial axis of the line. The second one [5] [4] [8] follows the line’s contours during the tracking. The medial axis is then obtained with the line middle

computation. It can be approximated, a threshold is used in order to control the polygonalization result. During the two trackings, the width of tracked line is computed. A case of violated width triggers the junction tracking process. This one uses line projections surrounding the current direction of medial axis (figure (1) (c)). These line projections allow to detect the starting lines of junction. The tracking process then slides on a junction zone in order to detect these starting lines.

Figure 1: Tracking Examples (a) pixel line tracking (b) surface line tracking (c) pixel junction tracking (d) surface junction tracking The surface based tracking uses surface element to track the shapes [2] [10]. These elements are used to compute the densities of black pixel into tracked zones. The maximum densities are then used to control the tracking process (direction and element size). Different element types are used : circle [2], gaussian bead [10], . . . . The figure (1) (b) gives an example of line tracking using a circle element [2]. During the tracking the progression into the line is of two types : continuous [2] or by jump [10]. In the last case the tracking provides vectorial data. The method of [2] labels too the bounded pixels by tracking element. If several zones of high density are detected a junction tracking process is triggered. This process draws circles and search intersections with the starting lines of junction in order to detect these ones [4] (figure (1) (d)). Several of existing works in the literature use a same element for the line and junction tracking : pixel element [3] [8] or surface element [2] [10]. Some others methods use a pixel element for the line tracking, and a surface element for the junction tracking [5] [4]. So these methods can be considered as hybrid methods. From our point of view the two methods can be compared according to some criteria : precision (of extracted medial axis), speed of tracking (opposed to complexity), and noise robustness. The table (2) gives a comparison of these methods according to these criteria. Through this table, we can see that the choice between tracking methods depends of image’s noise level and the wished precision.

Pixel Line Tracking Surface Line Tracking Pixel Junction Tracking Surface Junction Tracking

Precision high medium medium high

Speed high low medium medium

Robustness low high low high

Table 2: Comparison of Shape Tracking Methods The shape tracking methods allow to extract medial axis and junction from shape in one step. Like this, these methods are faster than classical methods like skeletonisation and contouring. In the other hand, the medial axis approximation and the junction positions seem of lower qualities [9]. From our point of view, these methods are especially adapted for the web indexing of graphic document images. However the indexing needs the automatic adaptation of these methods to image context (noise, type, . . . ). Indeed, an indexing application deals with unknown types of image. A tracking process using statistical evaluation of noise [6] in order to combine the different tracking methods seems a solution for this problem. 2

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