Speed-up Line Detection Approach for Large-size Document Images

by Parallel Pixel Scanning and Hough Space Minimization

H. Waruna H. Premachandra

, Chinthaka Premachandra

Chandana Dinesh Parape

and Hiroharu Kawanaka

ICT Center,Wayamba University of Srilanka, Makadura, Srilanka

Graduate School of Engineering, Tokyo University of Science Tokyo, Tokyo, Japan

Graduate School of Engineering, Kyoto University, Kyoto, Japan

Graduate School of Engineering, Mie University, Tsu, Japan

Keywords: Line Detection, Hough Space Minimization, Large Document Image, Parallel Pixel Scanning, Speed-up

Image Processing.

Abstract: Hough transform (HT) is typically used to detect lines in images, but that method is slow due to its use of

voting-based parameter detection; detecting lines in large document images can take dozens of minutes.

Nonetheless HT is very effective at detecting lines, so we investigate methods for fast HT-based line detection

of large document images by minimizing Hough space processing and reducing the image area used for line

detection with parallel pixel scanning and local image domain analysis. We conduct experiments to confirm

the effectiveness of the proposed method using appropriate large documents images. The results show a

significant computational time reduction as compared to conventional methods.

1 INTRODUCTION

Document image processing have widely been

studied and various studies of document scanning,

character recognition, and document structure

analysis and understanding can be found in literature

(You, 2010) (Wang, 2002) (Yip, 2001) (Manikandan,

2010) (Yang, 2000) (Borges, 2008) (Shi, 2013)

(Takasu, 1995). Document structure analysis and

understanding are mainly conducted by detecting

lines in documents that contain them.

Effective detailed document image analysis

requires a high-resolution image, but increased

resolution makes images larger. In this paper,

document images larger than 2000 × 3000 pixels are

considered large images. Image processing time for a

given target mainly depends on the image size and

complexity of the detection algorithm, so large

images can significantly increase processing time.

Thus, studies have been conducting processing time

reduction for large images through limiting the

number of processed pixels and developing simpler

algorithms (Premachandra, 2014) (Premachandra,

2014) (Premachandra, 2015).

Figure 1 shows a part of a large image used in this

study. The image contains characters, lines, and other

objects such as characters enclosed by ellipses. In this

study, the objects of interest are lines, for which we

develop a fast detection algorithm.

Generally, raster scanning is conducted while an

image is being processed. In raster scanning, the

image is scanned starting from the top-left point and

ending with bottom-right point. Processing large

document images generally requires considerable

computational time. In this study, pixel scanning is

conducted following the parallel vertical scanning

(PVS) and parallel horizontal scanning (PHS)

concept (Premachandra, 2013) (Premachandra,

2014). In PVS, pixels in horizontal lines are scanned

keeping a constant space between two consecutive

lines. With this scanning concept we can effectively

ignore characters and other uninteresting objects in

the image, reducing computational time. In the

proposed method, whenever a black pixel is found

while scanning we assume that the found pixel

belongs to a line, and line availability is estimated by

processing a defined local image domain (



) over

the black pixel. The approximate line inclination is

also determined through this estimation. Line

detection is then conducted by applying the Hough

transform (HT) process to a another extracted image

domain (



) following the determined approximate

Premachandra, H., Premachandra, C., Parape, C. and Kawanaka, H.

Speed-up Line Detection Approach for Large-size Document Images by Parallel Pixel Scanning and Hough Space Minimization.

DOI: 10.5220/0005840707650769

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP, pages 765-769

ISBN: 978-989-758-175-5

765

line inclination. As Fig. 2 and Eq. (1) illustrate,

Hough space (HS) is a conversion of (, )space to

(, ) space, here with the angle  ranging from 0 to

π. In this study we reduce the HS by limiting the

range of  regarding the determined line inclination,

significantly reducing computational time without

loss of line detection performance as compared to the

conventional HT process. This idea is one of the main

novelties of this paper. As mentioned above, line

availability estimation and line detection are

conducted by processing only small local image

domains (



,



) extracted from the original image.

This process allows further computational time

reduction, since the image area used for line detection

is very limited.

Figure 1: Part of a document image.

The new proposal was tested using appropriate

large document images, and the results showed a

significant computation time reduction with

improved line detection performance.

The remainder of this paper is structured as

Figure 2:(, ) → (, ) conversion.

=+ (1)

follows. Section 2 introduces conventional approaches

for line detection, and Section 3 details the new line

detection approach following PVS and PHS, local

image domain analysis, and HS minimization

concepts. Section 4 details experimental results of the

proposed method using large document images.

Section 5 concludes the paper.

2 RELATED WORKS

Line detection is an important step in analyzing

document images. Most studies in the literature

related to this task implement the Hough transform

(HT) ( Li and Tsai, 2011) (Zhao, 2010) (Aggarwal,

2006). Classical HT is commonly used to detect

regular curves such as lines, ellipses, circles, and

parabolas. For applications in which simple

descriptions of the desired features are not possible,

such as random handwritten objects detection, a

generalized HT can be used. Generalized HT is

conceptually similar to template matching and has a

higher computational complexity than the classical

HT, which itself is similar to template matching.

However, classical HT is also time consuming

because of its voting-based methodology. In this

study we use classical HT for line detection, to take

advantage of its high line detection performance. We

also aim at reduced time consumption without loss of

line detection performance.

Some studies perform line detection without

depending on the HT approach. Leferve et al.

(Lefevre, 2002) approached fast solid line detection

for scene modeling, focusing on horizontal and

vertical lines, and proposed a fast local approach to

line detection in binary images. In their method,

pixels are analyzed using an accumulator on a block-

based basis to obtain possible line segments for each

block. As another non-HT approach, Kawanaka et al.

(Kawanaka, 2007) conducted line detection by

connected component analysis. These two

approaches cannot detect lines that are connected

with other objects in the image. In addition, both

methods include connected component analysis,

which is time consuming when large objects exist in

the image.

3 PROPOSED LINE DETECTION

APPROACH

This section presents the line detection algorithm in

details.

VISION4HCI 2016 - Special Session on Computer VISION for Natural Human Computer Interaction

766

3.1 Proposed Algorithm

Step 1: The document image is binarized using

discriminant analysis (Otsu, 1999) and tilt correction

is conducted using the LPP method (Kawanaka,

2007).

Step 2: The image is scanned by PVS as illustrated in

Fig. 3. Here, the scanning lines are indicated by red.

Either of PVS or PHS can be used for scanning.

Figure 3: Parallel vertical scanning (PVS) and 



extraction.

Step 3: Whenever a black pixel of an object is found

while scanning, we extract a local image domain

(



) with the black pixel as its center point (Fig. 3).

The same label is set to all black pixels of that object

within 



Step 4: The minimum bounding rectangle 



of a

labeled object is calculated as illustrated in Fig. 4. In

addition, the aspect ratio 



of 



 and black

pixel density 



inside 



are calculated following

Eqs. (2) and (3). In Eq. (2), 



and 



(









) are aspects of 



. In Eq. (3), 



denotes the

number of black pixels inside 



Figure 4: Minimum bounding rectangle 







=



/



(2)





=













(3)

If 















, we assume that the

labeled object is part of a line and the process moves

Step 5. Otherwise the process moves to Step 7. In this

study, 



and



are experimentally determined.

Step 5: The inclination 



of 



is calculated. In

this study,  is not varied from 0 to π to generate the

HS by Eq. (1) as in conventional HS generation. We

instead determine the range of  according to 



Here,  ranges between 



 and 



+. We

set α=10

. With this setting HS generation can be

minimized, reducing computational time for HS

generation.

Step 6: We detect the line assumed in Step 4

following HT, only generating the HS using the range

for  determined in Step 5. Line detection is

conducted only when processing an image domain

(



) extracted from the original image. 



extracted over the assumed line in Step 4, specifically

considering its inclination (Fig. 5). HT is conducted

for only the extracted 



, further reducing

computational time. After conducting line detection,

the process moves to Step 7.

Step 7: The process moves to Step 3, in which the

next (, ) pixel is scanned.

The above algorithm effectively detects

horizontal and inclined lines, but not vertical lines

since it is difficult to cross the pixels of a vertical line

through PVS. We therefore implement the algorithm

using both PVS and PHS.

Figure 5:



extraction from the original image.

Speed-up Line Detection Approach for Large-size Document Images by Parallel Pixel Scanning and Hough Space Minimization

767

4 EXPERIMENTS

4.1 Experimental Setup

All experiments were conducted using a personal

computer with a Core i7 3.4 GHz CPU and 4 GB

RAM. Appropriate experimental materials were

created using a digital image scanner with a

resolution of 300 dpi. The size of those images was

2480  3508 pixels. We generated fifty document

images including 771 lines written in English,

Japanese, and Sinhala (the predominant language in

Sri Lanka).

To clarify detection performance, detected lines are

deleted by setting their pixels white. Specifically,

each pixel on a detected line and its eight neighboring

pixels we set to white.

We compared the proposed method with two

conventional methods from the literature: a

conventional HT line detection method and a line

detection based on connected component analysis

(CCA) (Kawanaka, 2007). The evaluation compared

line detection performance and computational time

consumption.

4.2 Results

Figures 6 shows an example image to which the

proposed method was applied for line detection. In

this figure, the upper image is the original image, and

the image below is the corresponding deletion result.

Here, some personal information is obscured by a

brown color.

Tables 1 and 2 summarize the results of the

experiments. As Tables 1 and 2 show, the proposed

method significantly reduced computational time and

dramatically reduced the number of false positives.

Table 1: Detection Performance Comparison.

Detection rate False positive

rate

98.7%

(760/771)

CCA

method

95.2%

(734/771)

10%

Proposed

98.4%

(759/771)

Table 2: Computational Time Comparison.

Average computational time

HT 1065 ms

CCA

method

765 ms

Proposed

89 ms

Figure 6: Line detection results.

5 CONCLUSIONS

We introduced a fast line detection approach for

application to large document images. In the

proposed method the image is scanned along parallel

vertical or horizontal lines, and line detection is

conducted by the HT while minimizing HS

processing by analyzing only local image domains

VISION4HCI 2016 - Special Session on Computer VISION for Natural Human Computer Interaction

768

selected from the image. Experiments were

conducted to evaluate the proposed method using

appropriate large images. The method significantly

increased computational speed as compared to the

conventional methods. The method furthermore

reduced the rate of false positives while maintaining

a line detection rate similar to the conventional

methods.

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