A Fast System for Drop Cap Image Retrieval Mathieu Delalandre
Jean-Marc Ogier
L3i, Pôle Sciences et Technologie Avenue Michel Crépeau 17042 La Rochelle, France 335.46.45.82.33
L3i, Pôle Sciences et Technologie Avenue Michel Crépeau 17042 La Rochelle, France 335.46.45.82.15
[email protected]
[email protected]
ABSTRACT
This paper deals with the retrieval of document images especially applied to the digitized old books. In these old books our system allows the retrieval of graphical parts and especially the drop caps. The aim of our system is to process large image databases. For that purpose, we have developed a fast approach based on a Run Length Encoding (RLE) of images. We use this last one in a image comparison algorithm using two steps: centering of images then a distance computation. Our centering step allows to solve the shifting usually met between scanned images. We present experiments and results of our system according to recognition abilities and processing times.
libraries of old books are available on the world wide web2 , 3 and will grow in the future [3]. Indeed, ambitious projects of digitization4 are under way like Google, Million Book and MSN projects.
Categories and Subject Descriptors H.3.1 [Content Analysis and Indexing]: Indexing methods; F.2.1 [Numerical Algorithms and Problems]: Com-
putations on matrices
General Terms
Algorithms, Performance, Experimentation
Keywords
Retrieval, Drop Cap, Image Database Formatting, Complexity, RLE, Compacity, Centering, Image Comparison
1.
INTRODUCTION
This paper deals with the topic of image retrieval and especially the document images. During the last years [1] many works have been done for the retrieval of journals, forms, maps, drawings, musical scores . . . . In this paper we focus on a new retrieval application: the one of old books1 (Figure 1). Indeed, since the Digital Libraries (DLs) development in the years 1990's [2] numerous works of digitization of historical collections have been done. Nowadays, large digital 1
We exclude in this paper the handwritten middle-age manuscripts previous to XV◦ century.
Figure 1: Some extracts of printed old book (a) Hippocrate "1532" (b) Bartolomeo "1534"
Dierent works have been proposed in the last years on digitized old books [4] like compression, pre-processing (lighting & curvature corrections, ltering, binarization . . . ), printed characters recognition, text/graphics separation . . . . The topic which interests us here is the retrieval of graphical parts. This one applies computer science techniques on digitized old books in order to retrieve, to segment and to match their existing graphical parts. Indeed, old books are mainly composed of printed characters but too of numerous graphical parts like headpieces, pictures, drop caps . . . . The Figure 2 gives some examples of these ones. In the paper's follow-up we present rst in section (2) a survey of existing graphics retrieval systems in the literature applied to old books. Through this survey we show how theses systems seem promising but use algorithms of high complexity and can't be applied on large image databases. We present then in section (3) our system for the fast retrieval of drop cap images and some experiments and results in section (4). Finally, we present in section (5) our conclusion and perspectives about this work. 2 http://gallica.bnf.fr/ 3 http://www.kb.nl/kb/manuscripts/ 4
http://en.wikipedia.org/wiki/Digital_libraries
Figure 4: (a) image 1 (b) image 2 (c) distance map bright parts correspond to low distances
Figure 2: Graphical parts inside old books
2.
SURVEY
It exists few works dealing with the topic of graphics retrieval inside digitized old books. This one is emerging and only some systems have been proposed during the last years: [5], [6], [7] and [8]. In [5] the authors propose a system based on orientation radiograms for the retrieval of gray-level ornamental images. The next Figure 3 explains the used process. For each pixel of a given image (a) a linear symmetry vector is computed. In geometrical terms, a linear symmetry vector corresponds to optimal straight-line tted to the local power spectrum. The computed vectors are next used to build the orientation images with a π/4 gap. The orientation radiograms (b) (c) (d) correspond then to the projection histograms of these images. These last ones are used as image signature and allow to compute a similarity distance between two images.
Figure 3: (a) image (b) radiogram 0◦ (c) radiogram 45◦ (d) radiogram 90◦ In [6] the authors propose an Hausdor distance based retrieval system for historical pictures. This system uses a sliding window for the computation of local Hausdor distances between binary images. Like this, one Hausdor distance is computed for each pixel of the query image. The sliding window's size is controlled according the wished precision. The global result of the comparison between two images is then a distance map corresponding to all computed Hausdor distances. An histogram is next computed from obtained distance map. It is then compared with the other distance map histograms (of previous comparisons) to establish a retrieval rank. The Figure 4 gives an example of computed distance map.
[7] uses a Zipf law based retrieval system applies on graylevel images of drop cap. In a rst step patterns are extracted from images. A pattern corresponds to a gray-level con guration of a pixel and its neighborhood. This neighborhood is de ned according to a 3 × 3 mask. Based on the 256 possible gray levels, and the use of a 3 × 3 mask, a large number (2569) of patterns can be de ned. In order to decrease this number the authors use dierent heuristics: a classi cation tool (a k-mean algorithm) of image gray levels into clusters, a cross mask instead of 3×3 mask. The Figure 5 (a) gives an example of extracted pattern.
Figure 5: (a) a pattern (b) Zipf curve Based on these clusterized patterns the system computes next a Zipf curve for each image. This one is obtained by ranking the extracted patterns from an image according to their frequency. The Figure 5 (b) gives an example of image and its corresponding Zipf curve. This Zipf curve is then used as image signature. The classi cation process is based on a k nearest neighbors (knn) algorithm between vectors corresponding to all the built Zipf curves of images. [8] exploits a segmentation process on gray-level drop cap images to segment textured and uniform regions. The Figure 6 (b) (c) gives examples of segmented regions. The segmentation process is decomposed into two main analysis: global and local. The global analysis is based on the computation of a uniformity criterion of regions using the gray-level cooccurrence matrix. This criterion allows to detect, and then to segment, the uniform regions on processed image. The other image parts are next used during the local analysis in order to segment the textured regions. This segmentation is decomposed in dierent steps: computation of a window size for a given texture class, feature extraction for each textured region (co-occurrence and run-length matrixes).
3.
A FAST SYSTEM FOR THE RETRIEVAL OF DROP CAP IMAGES 3.1 Introduction
Figure 6: (a) image (b) uniform regions (c) textured regions(d) MST of textured regions
In this section we present our system for the retrieval of graphical parts inside digitized old books. Especially, we have chose to apply this system to the retrieval of drop cap. As we have explained previously, we have based our works on complexity aspects more than retrieval accuracy of system. Then, for our system we have chose a very global retrieval use-case : the one of drop cap class. The next Figure 7 gives two image sets of same drop cap class. A drop cap class corresponds to printings produced by a same plug inside one, or between several, old book(s). Indeed, the plugs (usually in wood) were often re-used to print drop caps on books by printing houses during several years [10].
The segmented textured and uniform regions are next exploited to compute an MST5 based signature of image. The gravity centers of regions are then used to compute Euclidean distances between regions, and to build MST. Two MST are built, in independent way, from textured and uniform regions in order to reduce the time complexity of whole computation. The Figure 6 (d) gives an example of a computed MST from textured regions (c) segmented from the drop cap image (a). The MST are next exploited to compute signatures describing the spatial layout of regions. All these systems seem promising for the retrieval of graphical parts inside digitized old books. They are used for different query types: same images [5], equivalent drop cap styles [7], common regions [6], same layouts [8]. As we can see, they use all pixel based descriptors (patterns, windows, textures . . . .) instead of structural primitives [9] like lines, curves, junctions . . . . Indeed, digitized old book are very noised and presented lot of variabilities. It seems di cult to use classical approaches issue from graphics recognition [9]. However, the pixel based descriptors need computations of high complexity. In [5] the authors compute a linear symmetry vector for each pixel of an image using several orientations (with a π/4 gap). In [6], a large number of Hausdor distance are computed from a on sliding window. However, this distance is of quadratic complexity. In [7] the authors raise the complexity problems of their approach due to the pattern extraction step. At last, the system described in [8] uses several high complexity processes like the computation of co-occurrence and run-length matrixes. In regard to this situation existing systems of literature can't be applied, with realistic processing times, on real-life digital libraries of old books. However, these libraries [3] are composed of a large number of images (several thousands) and their sizes will grow again in the future. In this paper we propose a new system, for the retrieval of graphical parts inside digitized old books, opposed to existing ones. Indeed, this system is especially adapted to process large image databases. For that purpose, we have focused our works on complexity aspects more than retrieval accuracy of system. We present this system in the next section. 5
Minimum Spanning Tree
Figure 7: Classes of drop cap This retrieval use-case seems to be an image comparison application [11]. However, it raises an image shifting problem. Indeed, the drop cap images could come from dierent digital libraries. They were already used by dierent systems for their pre-processing (lighting & curvature corrections, ltering . . . ) and then segmented in dierent ways : automatic [12] [13], supervised by a user [14], and by cropping operation. The drop cap images of same class are then of dierent sizes and centered in dierent ways. In regard to this shifting problem and our complexity objective we have developed a retrieval system using two steps : one of Run Length Encoding of images and the other of RLE based image comparison (centering and distance computation). The next Figure 8 gives the general architecture of our system. In the section follow-up we present each of these both parts. We introduce previously a formatting process of image databases in the next subsection.
3.2
Formatting Image Databases
As we have presented previously the used drop cap images could come from dierent digital libraries. Then, it could appear lot of heterogeneity between these images in regard to the dierent following aspects : • Digitized images were produced by several institutes (libraries, companies, privates . . . ). Each of them use particular software and hardware for the digitization. • The digitization of historical collection endures for a long period (during several months or years). So, the used software and hardware of a given institute can evolve during this period.
Figure 9: Formatting process
Figure 8: System architecture • The digitization is a human controlled process, errors can appear. • The digitized images are previously processed for their cleaning and segmentation. These processings are performed by using dierent platforms like BookRestorer6 or Débora7 . So, the produced images depend of this used platform.
This heterogeneity raises dierent problems for a retrieval system. First of all an interoperability problem concerning the entry formats of processed images. Next, a semantic problem concerning the used resolution and compression mode (with lossless or not). In order to solve these problems we have developed, in entry of our system, a formatting process. This one is explained in the next Figure 9. It uses in three main steps : analysis, selection and formatting. During the rst one the image databases are analysed in order to extract some statistic about their features like : image types "binary, gray, color", the used formats "ti, jpg, png . . . ", the image resolutions, compression mode, size "in pixels". During this step the dual images (similar pixel to pixel) are also detected. Indeed, images can be exchanged between digital libraries, so it is necessary to detect these exchanges. Based on the analysis results, we de ne next a lter in order to set the second selection step. The aim of this step is then to select images among databases according to different constraints Cn on their features. Then, in a last step we format the images. For that purpose, we de ne a set of settings T {image type, format, compression . . . } that we use on previously selected images in order to format them according to the requirements of our retrieval system.
6 http://www.i2s-bookscanner.com/ 7
http://debora.enssib.fr
3.3
Run-Length Encoding of Images
Which interest us in our approach is to process, in a fast way, the image databases for the retrieval. So it is necessary to decrease the processing times of our algorithms, it exists two ways to do that. The rst one is to exploit special material architectures [15] like pipe-line processors or mesh-connected computers. However, the power growing of computers in the year 90's [16] has partially pushed down this way of research. The other way is to employ image representations adapted to used algorithm(s) in order to decrease the handling times of images. It exists few works dealing with this topic. [17] for example uses a contour based representation of images in order to perform fast neighboring operations like erosion, dilatation, skeletonization . . . . The authors in [18] propose them a system for the fast contouring of images exploiting a run based representation. At last, authors in [19] use a connected-component based representation to perform fast queries of image retrieval. Like this, all these systems exploit particular image representations according to their used algorithm(s). In our works we ourselves interested in the run based representation of images [9]. As explain in De nition 1 the run allows to encode successive pixels into one data structure. Like this, the size of used data to represent an image is reduced, as soon as the handling time. The conversion of a raster based representation to a run based representation is called Run-Length Encoding (RLE) in the literature. The next Figure 10 gives an example of RLE.
De nition 1 A run is maximal sequence of pixels de ned according three features {o,(x, y),l} where:
o is the run orientation (either vertical or horizontal) (x,y) is the starting point of run l is the run length
Figure 10: (a) raster (b) RLE The rst run based system has been proposed by [20]. Then, several ones have been developed during the years 90's [9] mainly in the eld of graphics recognition. Nowadays, the runs are used for several dierent applications : handwriting recognition [21], symbol recognition [22], structured document segmentation [23] . . . . However, few of existing systems deals with the use of run in regard to the complexity aspects. Indeed, the runs have been widely used to de ne representation graph (LAG8 , VSG9 . . . ) for pattern recognition purposes. We argue here than the run based representation can be evaluated, in regard to complexity aspects, according to two criteria : the compression rate and the RLE type. We present each of them in the follow-up of this subsection. The compression rate tc (Equation 1) de nes itself, in a natural way, as the ratio between the number of run nr and the number of pixel np of an image. From our point of view, this rate can be used also to determine a compacity of runs. In our mind, this compacity corresponds to the way in which the runs are grouped on image. The next Figure 11 gives two examples of image of strong and weak compacity. So, we propose to de ne this run compacity cr as the opposite of compression rate (Equation 2). Nevertheless, considering the exponential property of this function we prefer to use the logarithmic expression ln t1c .
tc =
nr np
nr ∈ [0, np ] tc ∈ [0, 1] 1 cr = ln cr ∈] + ∞, 0[ tc
(1)
Figure 12: Run compacity curve Another criterion to evaluate a the run based representation is the encoding type. Indeed, existing works in the literature [9] apply usually (for their pattern recognition purposes) the encoding to image foreground (ie. black pixels). However, this encoding can be applied to foreground and/or to background. In this way, three encodings can be considered as presented in Figure 13 : on foreground (a), on background (b) and on foreground and background (c). They can be extended if we considered the vertical and/or horizontal encodings of runs. We will talk here about simple (a and b) and dual (c) encoding to refer these ones. Each of these encodings has advantages and drawbacks. The simple encoding presents better compression properties : only a part of image pixels are used. In the other side, it focusses the image representation on the considered part (foreground or background). The dual encoding allows a full image representation. Obviously, it decreases the compression rate, whole pixels of image are encoded.
(2)
Figure 13: (a) raster (b) foreground RLE (c) background RLE (d) foreground/background RLE Figure 11: compacity (a) strong (b) weak The next Figure 12 gives the curve of run compacity (cr ) according to the compression rate (tc ). On this curve we distinguish two main zones : one of low tc ' [1 − 0.2] and strong tc ' [0.2 − 0] growth. Like this, this curve shows that the RLE allows an easy compression of image until a compression rate of tc ' 0.2. Strongest rates will be more di cult to obtain and will depend of processed image types. 8 Line 9
Adjacency Graph Vertical Simple Graph
For the purposes of our comparison algorithm we have chose to use a dual encoding. Indeed, this one seems more adapted for the comparison of drop cap images. The background of these images describes objects (letter, character(s) . . . ) as soon as the foreground. We perform this encoding from binary or gray-level images. In the second case, a xed threshold binarization algorithm is used. At last, in regard to the shifting problem of drop cap images we perform an horizontal and vertical encoding. We present our comparison algorithm in the next subsection.
3.4
Pseudo-algorithm 3.1: distance(i1 , i2 , dx , dy )
Run based Comparison of Images
In this subsection we present our algorithm for image comparison exploiting our dual RLE (foreground and background) using vertical and horizontal runs. As we have presented previously, the drop cap images processed in our system present a shifting problem. In order to solve this one, our comparison algorithm uses two steps : one of image centering and one other of distance computation. We present each of them in the follow-up of this subsection. Our image centering step exploits horizontal and vertical projection histograms of black pixels. These histograms are built by handling the vertical and horizontal runs. The Figure 14 gives some examples of built histograms. These ones are computed from black pixels in regard to the segmentation process previously used on whole document images to extract the drop cap. Indeed, this process involves the adding of borders of background color around the drop cap. The centering settings can be found then, in a natural way, using the analysis of black pixels.
Figure 14: Vertical and horizontal histograms We center then the two images together by computing distances between their couples of histogram. Especially, we have chose the weighted distance presented in Equation 3. Indeed, the weighting increases the robustness of comparison when strong amplitude variations exist inside histograms [24]. Our images could be sized in dierent ways, so our weighted distance is computed using an oset {0, ., l − k}. The delta to use (ether x or ether y) corresponds then the found minimum distance among computed osets. The previous Figure 14 presents two images with deltas dx = 1 and dy = 4.
s←0
x1 ← x2 ← 0
a1 ← a2 ← 0 and L2 at y + dy of i2 p1 ← next(L1) x1 + = p1 .length p ← next (L2) x 2 2 + = p2 .length while (p = 6 end) ∨ (p 1 2 6= end) whilex1 ≥ (x2 + dx ) if p2 .color = p1 .color then s+ = p2 .lenght − a2 p2 ← next(L2) do x2 + = p2 .lenght a1 + = p2 .lenght − a2 do =0 do while (xa2 + dx ) ≥ x1 2 if p1 .color = p2 .color then s+ = p1 .lenght − a1 p 1 ← next(L1) do x + = p .lenght 1 1 a + = p .lenght − a1 2 1 a1 = 0 s ← s/(min(i1 .width, i2 .width) × i1 .height)
for each line L1 at y of i1
Our algorithm uses the vertical runs to compare two given images i1 et i2 . It browses all the lines L1 and L2 of these images at coordinates y and y + dy . For each couple of line, it browses alternately the runs using two variables {p1 , p2 }. The Figure 15 explains this run browsing. Two markers {x1 , x2 } are used to indicate the current positions of browsing. The browsed line is already the one of lower position (tacking into account the dx oset of centering step). The latest read run of upper position is used as reference run. The runs of browsed line are summed using a variable s if these ones are of same color as the reference run. During the alternately browsing two stacks {a1 , a2 } are used. These last ones allows to deal the browsing switches (L1 L2). For that purpose, they sum the browsed distances on each line using the reference runs.
Figure 15: Run browsing g1,2,..,k h1,2,..,l k≤l
delta = min
l−k [
k X
j=0
i=1
|(hi − gi+j )| hi
!
(3)
In a second step we compute a distance between our images. This distance is obtained by (simple) comparison pixel to pixel [11]. However, to compute this distance our algorithm uses obviously our run based representation. Then it needs to be adapted to this last one, we present this algorithm below10 . 10
Presentation based on the LATEX package Pseudocode [25].
3.5
Conclusion
Our system uses then two main steps to perform the retrieval process : a rst one of RLE from rasters and a second one of image comparison. Concerning the RLE this one is performed in one-shot when images are added to our database. So, our retrieval process works directly on encoded images. Our image comparison uses her two successive steps : one of centering and one other of distance computation. Concerning the centering step the used histograms are directly computed in one-shot too during the RLE of images. Indeed, these histograms are complementary meta-data of run based representation, so we have stored them in our run les.
Like this, our retrieval process is mainly based on the distance computation step of image comparison. This one needs only two browsings of runs to compute the distance, one on query image and one other on tested image. We present in the next section some results of our system in regard to retrieval abilities and processing times criteria.
4.
EXPERIMENTS AND RESULTS
Our works are in progress, in this section we present our rst experiments and results11 . For that purpose we have tested our system on an drop cap image databases provided by the BVH12 . We present our results on this database concerning four aspects : formatting, analysis of compression rates and run compacities, processing times and query example. Before starting our retrieval process we have analysed our drop cap image database in order to plan its formatting. The results of this analysis are presented on Table 1. This database is composed of a large thousand of image for a total size of about 800 Mo. This size has been computed by considering all images as uncompressed and in gray-level. The images are typed in gray-level (8 bits) as soon as in color (24 bits). The used formats are Ti and JPG using compression modes without lossless (PackBits and JPG of high quality). The main problem of this database concerns the variations of resolution between images. Indeed, these ones start from low levels (72 dpi) to high levels (399 dpi). Moreover, for a large part of this database the used resolutions are unknown because unwritten inside image les.
Size: Depth: Format: Compression: Resolution
Files
1382 8 bits 57.89 % Ti 49.93 % None 0.43 % Unknown 32.13 % 300 dpi 51.67 %
Octets
833.68 Mega 24 bits 42.11 % Jpg 50.07 % PackBits 49.49 % 72 dpi 12.95 % 399 dpi 0.14 %
Jpeg
50.08 % 3.11 %
200 dpi
Table 1: Features of our image databases In regard to these results we have done the formatting of this database. For that, we have selected all images of 300 dpi and then formatted them in gray-level and stored without compression in Ti format. The obtained new database is composed of 714 images with a size of 489.6 Mo. We have next done the RLE of images composing this database in order to extract features about their compression rates and run compacities. We have obtained a mean compression rate of tc = 0.143 and a mean run compacity of cr = 1.94. These results have been obtained by meaning the horizontal and vertical encodings. These results show that RLE has reduced of 85 % the size of images, so from 6 to 7 times. The Figure 16 gives some examples of drop cap images with their compression rates.
Figure 16: Drop cap images and compression rates We have evaluated then the processing times of our system. This one has been implemented in C++ language using a Java control application. We have tested this system on a laptop computer using a 2GHz Pentium processor and a Windows XP System. Our results are presented in Table 2.
Global times
Reading Query (small) Query (large)
(seconds) 108.93 s 255.6 s 384.78 s
Speed
(Mega octets / minute) 269.4 Mo/mn 115.8 Mo/mn 76.2 Mo/mn
Table 2: Processing times In a rst step we have evaluated the reading time of our image database : so only one browsing of runs for each image. We have obtained a result time of 108.93 s on whole database, so a reading speed of around 270 Mo/mm. In a second step we have evaluated the processing times of our queries. These times depends of considered query image. Indeed, the query image is handled at each comparison, so a biggest size involves a more longer retrieval process. We have performed thirty queries and obtained processing times from 255.06 s to 384.78 s, thus a speed around 100 Mo/mn. These times corresponds to the two run broswings of images with the comparison of histograms. At last we have performed, in a random way, some queries in order to evaluate the retrieval abilities of our system. The next Figure 17 gives an example of query result. In regard to this kind of result13 our system seems allowing an e cient retrieval of drop cap images of same class. Indeed, as explained previously in the paper the main problem of this retrieval application is the shifting introduced during the segmentation process. However, our centering step allows to correct it with a good accuracy. The remained retrieval problem met by our system concern the very damaged drop cap images. These damages can have several reasons : broken plug, ripped parts , darkness paper, bad curvature . . . .
5.
CONCLUSION AND PERSPECTIVES
In this paper we have presented a system dealing with the document image retrieval applied to the digitized old books. In these old books our system is applied to the retrieval graphical parts and especially the drop caps.
11
More results will be included in the nal version of this paper. 12 http://www.bvh.univ-tours.fr/
13
A groundtruthing process is under progress in order to provide full retrieval results.
Conference on Politics and Information Systems: Technologies and Applications (PISTA), Vol. 1, 2004, pp. 136141. [4] F. Lebourgeois, al, Documents images analysis solutions for digital libraries, in: Workshop on Document Image Analysis for Libraries (DIAL), 2004, pp. 224. [5] J. Bigun, S. Bhattacharjee, S. Michel, Orientation radiograms for image retrieval: An alternative to segmentation, in: International Conference on Pattern Recognition (ICPR), Vol. 3, 1996, pp. 346350.
Figure 17: Example of query result The aim of our system is to process large image databases. For that purpose, we have developed a fast approach based on a Run Length Encoding (RLE) of images. This one allows to reduce the image sizes and then their handling times by algorithms. The central part of our system is an image comparison algorithm. This one uses two steps : centering of images following by their distance computation. Like this, the centering step allows to solve the shifting problem usually met between scanned images. We have presented dierent experiments and results about our system. We have shown the existing formatting problems among image databases and shown how our system allows to solve them. We have shown next how our system allows to compress from 6 to 7 the image size, and therefore a faster comparison of these ones. The comparison speed of our system is then of around 100 Mo/mn. At last, we have shown the retrieval abilities of our system through some query examples. The perspectives concerning this work are of two ways. In a rst step we which used this system for groundtruthing our image database. The idea is to provide the results of our system to a user. Like this, this one will be able to edit a groundtruth about images in a semi-automatic way. In a second step we which extend our system to the local comparison of images. The idea is then to retrieve speci c graphical objects like characters, furnitures . . . . For that, we work currently on a run based signature based on the [26] works.
6.
ACKNOWLEDGMENTS
The authors thank the CESR , and especially the BVH project's members, for providing the images used in this works.
7.
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