Abstract—During the past few years, automatic check processing has become a popular topic in the field of document image analysis (DIA). Image binary is an important but hard task in automatic check image processing system. Difficulties in binary procedure derive mainly from the different types and positions of seal imprints. In this paper, we proposed a new binary method which is based on the fact that objects in a character image are mostly dark with thin strokes. In order to remove large block backgrounds, we firstly designed a set of morphological perorations to enhance the local feature of thin objects. Then a global threshold is selected and applied to the new enhanced image. Experiment results on 2745 real-life Chinese check images demonstrate the efficiency of our method compared with other commonly used binary methods. The recognition success rate has achieved 91.5%.

I. INTRODUCTION

URING the past few years, automatic check processing has become a popular topic in document

analysis, and is becoming one of the most promising commercial applications of handwriting recognition [1-3]. In China, thousands of checks will be validated due to manual processing every day, it is necessary to relieve people from these intensive and tedious works.

Thresholding a gray-level image into two levels is the first step and also a critical part in most document image analysis systems since any error in this stage will propagate to all later analysis. Various algorithms have been proposed over past years, however, with a complex background, problem remains unsolved.

Binarization techniques can be categorized into two classes: global and local. Global thresholding algorithms [4-9] use a single threshold. Otsu’s [6] method is efficient when clear background and foreground exist. For check image with complex backgrounds, Amer Dawoud and M. Kamel [7] proposed model-based binarization algorithm and M. Cheriet [8] proposed Extending Otsu method. According

This work was supported in part by Nanjing University of Science and

Technology (AB41137).

Chongyang Zhang is with the Department of Computer Science, Nanjing

University of Science and Technology, Nanjing 210094 China. (Phone:

+8602584317297-415; e-mail: zcy603@163.com).

Jingyu Yang is with the Department of Computer Science, Nanjing

University of Science and Technology, Nanjing 210094 China.

to stroke features, we proposed an effective recursive segmentation technique [9] that continues removing the brighter background until only the darkest object is left. An obvious drawback of these global techniques is that it cannot separate those areas which have the same gray level but do not belong to the same part.

Local thresholding algorithms [10-16] compute a separate threshold for each pixel based on a neighborhood of the pixel. In [10, 11], Trier and A. K. Jain had given the comparative study of eleven most promising local algorithms. In recent years, stroke model based local features [15, 16], which consider the restriction of stroke width, were developed for binarizing document images. Local thresholding algorithms usually take sharply changed foregrounds as object.

Binarizing images with complex backgrounds is still a difficult task. It is even more challenging for courtesy amount images in Chinese check. Difficulties in binarizing these images derive mainly from the different types and positions of seal imprints. Fig. 1 shows a typical Chinese check image. Seal imprints were printed randomly on the check, and some overlapped with user entered information, such as legal amount, date and courtesy amount.

Fig. 1 A typical Chinese check image

In this paper, we proposed a new check image binary method, which combine local and global techniques. First, a morphological method is designed to enhance the objects in gray level image. Then the new stroke enhanced image is threshold by a global threshold. In the next section, our method is introduced. Then experimental results and comparisons are given in section 3. Conclusions are drawn in the last section.

Check Image Binary with Morphological Method

Chongyang Zhang, and Jingyu Yang

D

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Third International Workshop on Advanced Computational Intelligence August 25-27, 2010 - Suzhou, Jiangsu, China

978-1-4244-6337-4/10/$26.00 @2010 IEEE

II. METHOD

In the following analysis, foreground is supposed to be dark (with lower gray value) and background is supposed to be white (with higher gray value).

There are images that objects can’t be separated from seal imprints with a single global threshold, since their corresponding area in gray level histogram cross. Seal imprints can be taken as two parts: frame part and character part. Frame part is usually the darkest background in an image. A stroke in a document image is usually thin and connective, its width is usually under a predefined range. Compared with strokes, frame parts of seal imprints can be seen as large block areas. Thus we can design a local method to enhance the strokes before image thresholding.

(a)

(b)

(c)

(d)

Fig. 2 Ideal 1-D profile models and examples

(a) 1-D profile models of strokes large block objects; (b) raw gray image;

(c)(d) profile images of (b) in row 36 and 60 separately.

A. Morphological enhanced method

In a one-dimensional (1-D) profile, a stroke and a large block background can both be modeled as a field of connected pixels with low gray levels, while the run length of

the former is smaller than that of the later. Fig. 2 is an ideal 1-D profile and an example of 1-D profile of a character image in row 36 and 60.

Mathematical morphology has been widely used in the area of image processing. It is efficient in shape analysis problems. The basic mathematical morphological operations are dilation ( ⊕ ), erosion ( Θ ), opening ( ) and closing ( • ). They are defined as:

)}(),(),,(),(max{),( , EDbabaEyxfyxEf ba ∈+=⊕

))}(),(),,(),(min{),( , EDbabaEyxfyxEf ba ∈+=Θ

EEfEf ⊕Θ= )(

EEfEf Θ⊕=• )(

Where, ),(),(, byaxfyxf ba −−= ; )(ED is the defined

field of E ; f is a gray level image; E is a morphological

structure element. The linear closing operation can be used to extract long

features within a 1-D profile image. In order to extract two-dimensional (2-D) stroke features which are mostly narrow, the following operations are proposed:

)())(()( 30 pfEpfMaxpLMDE ddW −•= =

Where, dE refer to a liner structure element whose length

is W in the direction of d ; d =0, 1, 2, 3 refers to 0, π/4, π/2, 3π/4 separately; W is corresponds to the largest stroke width of characters to be extracted. In our experiments, stroke width is mostly between 3-5 pixels, we select W =5. Fig. 3 shows four liner structure elements.

Fig. 3 Four liner structure elements

LMDE is a local feature that describes the strokes. Stroke objects can be extracted from the complex background of seal imprints (character seal and frame seal) by considering both gray feature and LMDE feature. Stroke object usually is dark and thin, and pixels belonging to it have high LMDE value. Character seal background is brighter than stroke object, and the LMDE value usually would not exceed that of the objects.

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Frame seal background is a large block area, and pixels belonging to it usually have very low LMDE value.

(a)

(b)

(c)

(d)

(e)

(f) (g)

Fig. 4 An example of image enhancing with morphological method

(a) raw gray image; (b) local stroke edge feature image of (a); (c) stroke

enhanced image of (a) with morphological method; (d) binary image of (a)

with a global gray threshold 66, which is selected by human; (e) binary image

with our method; (f)(g) gray level histograms of (a) and (c) separately.

We produce the enhanced image with LMDE feature:

LMDEfg ⋅−= α

Here, α is an experience factor. Fig. 4 is an example of enhanced image with morphological method. In Fig. 4(c), narrow objects, such as character strokes and background texture, are enhanced to be darker, and the enhanced intensity is related to its local contrast. Fig. 4(d) is the binary image of (a) with a global gray threshold 66, which is selected by human. It is obvious that there is no proper threshold for this example image. Strokes have been split, while seal imprints

have not been removed. From the gray level histograms of the raw image and its morphological enhanced image, we can see that extracting characters became possible, since after the enhancing operation, stroke field in the histogram moves to the left (lower value).

B. Threshold selection

The global threshold is selected by over segmentation. If a result image contains only the stroke objects and be segmented once more, the new removed pixels mostly around the pixels of the new result image, since the stroke object is thin and stroke center pixels are darker.

We define the distance between a pixel p and a pixel-set Q as the distance between p and its nearest pixel in Q .

By applying [6], a given image nI is threshold to two sub images, dark one 1+nI and bright one 1' +nI . ip is one of the pixels in 1' +nI , and id represents for its distance to 1+nI . If

rdi > , we call ip a FAR-pixel according to 1+nI . If the number of FAR-pixels in 1' +nI is less than δ , image nI is considered to obtain only one darkest object. Here, r and δ are two predefined thresholds. r usually is set to half stroke width. δ is related with the noise of the image.

Fig. 4(e) is the binary result of Fig. 4(a) with our method, δ =100. Dark seal imprint has successfully been removed.

III. EXPERIMENT RESULTS

We carry out experiments on a test set of courtesy amount images obtained from 2745 gray-level real-life Chinese check images scanned with a resolution of 200 DPI. To evaluate the performance and effectiveness of the proposed method, we compared it with some other methods: Cheriet’s extending Otsu method [8], Enhanced Bernsen’s method [12] and Kamel-Zhao’s logical level technique (LLT in brief) [15]. These three are common used binary methods (global, local and stroke model based) for document images.

Fig. 5 gives the comparison results of a courtesy amount image. Extending Otsu takes the dark pixels in background as objects. LLT often removes the stroke pixels within seal imprints. Local Bernsen’s method is suffered from the sharply varying edges in background. Our method is efficient in preserving the dark and thin objects in a given image.

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(a)

(b)

(c)

(d)

(e)

Fig.5 Comparison of image binary results

(a) raw image; (b) binary with extending Otsu; (c) binary with LLT; (d)

binary with local Bernsen; (e) binary with our method.

To evaluate the performance and effects of our methods for later process, we give the recognize results of these courtesy amount images. Binary process is performed on images whose lines have been removed. Line remove method can be seen in ref. [9].

An image is correctly recognized, only when all characters in this image are recognized correctly. Table 1 is the recognition results with different image binary methods. Our method is efficient compared with the other four segment methods, and the recognition success rate has achieved 91.5%.

TABLE I

RECOGNITION RESULTS WITH DIFFERENT IMAGE BINARY

PROCEDURES (TOTAL 2745) Binary method Failure Success Rate

Recursive Otsu 518 81.1%

Enhanced Bernsen 811 70.5%

LLT 653 76.2%

Our method in [9] 258 90.6%

Our method 233 91.5%

IV. CONCLUSION

In Chinese, thousands of checks will be validated due to manual processing every day, it is necessary to relieve people from these intensive and tedious works. Extraction of items from the check images with complicated backgrounds remains as one of the most challenging topics in the field of automatic check processing system. In this paper, we proposed a new image binary method based on morphological stroke enhancing method.

The detection of the local stroke edge feature and image enhancing with morphological method is easy implement and suitable for parallel processing and hardware implement. Compared with previous works, great improvement is achieved by our methods. The recognition success rate reaches 91.5%.

Stages that may produce failure include not only image binary but also many other process stages (such as item location, line removal, string segmentation and character recognition).

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