Character Segmentation and Recognition Algorithm of Text Region in Steel Images

Transcription

1 Character Segmentation and Recognition Algorithm of Text Region in Steel Images Keunhwi Koo, Jong Pil Yun, SungHoo Choi, JongHyun Choi, Doo Chul Choi, Sang Woo Kim Division of Electrical and Computer Engineering Pohang University of Science and Technology Pohang, Korea {khkoo, rebirth, csh425, y2kscore, dooshe, Abstract: - There are many algorithms for segmentation [1]. Many people have been making researches in segmentation of touching or overlapping up to now, but most of algorithms cannot apply to the text region of slab management numbers marked on the slab in the steel. Because the text regions are irregular such as touching by strong illumination and by trouble of nozzle in marking machine, and loss of. It is difficult to gain high success rate in all cases. This paper proposes a new algorithm for segmentation using combined profile analysis and recognition based method. Besides the proposed algorithm converts gray to binary using method of adjusting brightness and contrast in pre-processing step. The experimental results show high recognition rates of slab management numbers marked on the slab about various text region s. Key-Words: -, numeral, segmentation, steel, slab, management number 1 Introduction It is necessary to recognition system using digital processing for automation of the steel production [2]. The recognition system is composed of three steps such as text localization, segmentation, and recognition. The segmentation is difficult because of noise, illumination, and so on. For high recognition rates of the s and the system, it is necessary to good performance of the segmentation algorithm. This paper describes a new algorithm of segmentation which is based on recognition. It is very important that pre-processing step is to convert gray to binary without touching and loss of. In the binary, non-touching s are simply separated by using vertical projection profile. For separating touching s, after we use combined profile to find candidate points of boundary, decide real s using method based on recognition. The Separated s are recognized by using Support Vector Machine (SVM) [3]. In this paper, the proposed algorithm is effective for the segmentation of the text regions on the slab in the steel. After describing feature of the extracted text regions in section 2, we give a brief explanation of previous segmentation algorithm in section 3. We present the proposed algorithm in section 4 and provide experimental results in section 5. Finally, we conclude in section 6. 2 Feature of the extracted text region Fig.1 Slab Text regions are extracted by using the text localization algorithm [2] in Fig. 1. Fig. 2 shows various kinds of the text regions. General text region s (Fig. 2 (a)) are separated by simple method (vertical projection profile of binary ). Text regions with burned s (Fig. 2 (b)) need to represent dim s in binary. Lastly, text regions which are affected by strong illumination and by trouble of nozzle in marking machine (Fig. 3 (c) (d)) induce touching. Because most segmentation algorithms for various kinds of the text regions show bad performance, we need to be new algorithm which has high recognition rates. In this paper, the proposed algorithm considers various kinds of the text regions in Fig. 2. ISSN: ISBN:

2 4 Proposed algorithm In this section, we detail the new algorithm for the segmentation. The extracted text region marked SMNs is gray and SMNs are the printed. SMNs are consist of 9 s. In pre-processing step which performs to convert gray to binary, we improves a performance of binarization. Preferentially, the s are simply separated by using vertical projection profile () in the binary. If a touching is appeared, the touching is separated by using the new method that analyzes the combined profile and uses the recognition based method. The separated s to SMNs are recognized by using One-to-One SVM [7]. The detailed contents follow. Fig. 2 Extracted text region s 3 Previous segmentation Previous algorithms for segmentation are classified by three types such as dissection method, recognition based method, and holistic method [1]. Recently, many people have made the profound study of touching. For example, there are methods which use Multi-Layer Perceptron (MLP) of neural network [4], Log-Gabor filter [5], or foreground and background analysis [6]. It is also important to separate touching in the text region marked slab management numbers (SMNs), but these methods do not apply to the target s of this paper due to the feature of the extracted text region represented in section 3. After this paper separates touching using combined profile analysis, uses recognition based method for choosing correct boundaries of the touching. Fig.3 Proposed algorithm 4.1 Pre-processing The goal of pre-processing is to convert the gray to the binary which represents distinct boundaries of s and dim s. Fig. 4 shows the method of pre-processing. Because totally bright text regions have ambiguous boundaries of the s, need to adjust brightness ISSN: ISBN:

3 and contrast. If mean value of the gray intensity is more than.65 (gray intensity normalized from to 1), the brightness of the gray is totally decreased by gamma function [8] and then the contrast is increased for distinguishing boundaries of the s (Fig. 5). Fig. 4 Pre-processing Fig. 7 Result of pre-processing (Binarization) Fig. 5 Adjusting brightness and contrast in gray Since burned s look like dim in the text region, the s disappear in the binary. When converting to binary, we apply Otsu s binarization [9] method not to the whole region but to the respective local regions (Fig. 6). Fig. 7 shows the result s in the pre-processing. Fig. 6 Multi-local Otsu s binarization 4.2 Character segmentation using At first, proposed algorithm uses of the binary obtained in section 4.1 for the segmentation. is to represent the total number of white pixels in vertical direction of the binary to graph. Because boundaries of the s are certainly regions composed of background in the vertical direction as value of is zero, the text region is separated at these regions. While all s are separated such as Fig. 8 (a) (b), touching s occur such as Fig. 8 (c) (d). When length of width in separated is longer than.8 times length of height (feature of the printed in the slab ), it is judged that the separated s are touching. ISSN: ISBN:

4 Proceedings of the 8th WSEAS International Conference on SIGNAL PROCESSING, ROBOTICS and AUTOMATION (a) Character segmentation without touching (b) Character segmentation without touching Fig. 9 and TDP analysis (c) Character segmentation with touching Calculating score graph Score( c) = (1 r) Gi ( c+ r) (1) i= 1 r= 5numberOfpoint( i) c : c st column i : i st graph Gi ( k ): value of k st point in i st graph (zero or one) numberofpoint(i) : total number of candidate points in i st graph (d) Character segmentation with touching width >.8Xheight : touching Fig. 8 Character segmentation using 4.3 segmentation Method for separating touching consists of 4 steps such as extracting all candidate boundary points, calculating score graph, choosing combined boundary points, and choosing correct boundary points Extracting all candidate boundary points Boundaries of touching s are located at the valley points of or TDP (top down profile). TDP is to represent position of first white pixel at respective column to graph. Because all valley points are not boundaries of touching s, all candidate boundary points are extracted such as Fig. 9. For and TDP analysis, binary, feature binary, and gray are used. White pixels in the feature binary are composed of peak, hillside, and ridge points of topographic feature in gray [1]. All candidate boundary points extracted in section are combined to the score graph using equation (1) such as Fig. 1. Real boundary regions of s have a large value at the score graph Choosing combined boundary points Combined boundary points are selected from the score graph. Because real boundary of s uniquely exists within the range of.8 times length of height in the score graph using feature of the printed, we choose the points which have maximum value within this range. These are the combined boundary points, but not exact boundary points. For that reason, the touching s are separated by minimum path in gray scale of the surrounding regions of these points using multi-stage graph search algorithm [11]. Fig. 11 shows the separated s from the touching Choosing correct boundary points When finding the combined boundary points more than real boundary points, we should choose correct boundary points. After making up all cases which are able to separate the touching from the combined boundary points such as Fig. 12, the proposed algorithm selects the correct case that has minimum distance between the separated s and the representative s using recognition-based method [12]. The representative displays the recognition result of the separated. ISSN: ISBN:

5 case 1 Representative case Selection Score graph Representative case 3 Representative Fig. 12 Choosing correct boundary points 4.4 Recognition Input width/height Yes <.4? 1 No Vector(48X36) vs. 2 SVM vs. 3 SVM vs. 4 SVM To select of maximum vote 7 vs. 9 SVM 8 vs. 9 SVM 7 8 Fig. 1 Score graph Fig. 11 Combined boundary points Fig. 13 Recognition For recognizing the separated s in section 4.3, the proposed algorithm uses One-to-One SVM and width to height ratio. Preferentially, because width to height ratio of number 1 is definitely small, number 1 is recognized when width to height ratio is less than.4. The others are recognized by using One-to-One SVM that has a good performance of Multi-class SVM. Fig. 13 represents recognition process. 5 Experimental result and analysis The proposed algorithm can separate the s for the various text region on the slab (Fig. 14). In this paper, the new algorithm of the segmentation and recognition is applied to 1923 text region s marked SMNs, and makes a success of 127 s. Experimental results are represented to Table 1. The fail s of the experimental results are classified by 4 cases; bad which has the worst quality, fail of 9 ISSN: ISBN:

6 recognition, fail of pre-processing, and fail of segmentation (Table 2). For improved performance, we need to advanced recognition, binarization, and segmentation of touching. Fig. 14 Separated s Table 1 Result of success rate Total Success Fail Success rate 94.2% Table 2 Analysis of fail s Bad Fail of recognition Fail of Pre-processing Fail of Segmentation % 1.56% 1.45% 1.9% 6 Conclusion In this paper, the new algorithm can separate various text region s marked SMNs in respective slabs, and then recognize. We improved a performance of binarization in pre-processing, and proposed new method separating the touching using combined profile analysis. Finally, because the proposed algorithm shows a good performance in the experimental results, it is effective that the algorithm is applied to recognition system. Acknowledgements This research was financially supporting by the Ministry of Education, Science Technology (MEST) and Korea Industrial Technology Foundation (KOTEF) through the Human Resource Training Project for Regional Innovation. References: [1] Richard G. Casey and Eric Lecolinet, A Survey of Methods and Strategies in Character Segmentation, IEEE Trans. On Pattern Analysis And Machine Intelligence, Vol. 18, No. 7, Jul., [2] SungHoo Choi, Jong Pil Yun, KeunHwi Koo, JongHyun Choi Sang Woo Kim, Text Region Extraction Algorithm On Steel Making Process, 8th WSEAS Int. Conf. on ROCOM'8, Hangzhou, China, Apr., 6~8, 28. [3] Nello Cristianini and John Shawe-Talyor, An Introduction to Support Vector Machines, Cambridge University Press, 2. [4] Jin Hak Bae, Kee Chul Jung, Jin Wook Kim, and Hang Joon Kim, Segmentation of touching s using an MLP, Pattern Recognition Letters, Vol. 19, No. 8, 1998, pp [5] C. Mancas-Thillou and B. Gosselin, Character segmentation by recognition using log-gabor filters, in Proc. ICPR, Hong Kong, China, Vol. 2, 26, pp [6] Yi-Kai Chen, and Jhing-Fa Wang, Segmentation of Single- or Multiple- Handwritten Numeral String Using Background and Foreground Analysis, IEEE Trans. On Pattern Analysis And Machine Intelligence, Vol 22, No. 11, Nov., 2. [7] Dejan Gorgevik, Dusan Cakmakov, Combining SVM Classifiers for Handwritten Digit Recognition, IEEE, 22, pp [8] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Second Edition, Prentice Hall. [9] N. Otsu, A Threshold Selection Method from GrayLevel Histogram, IEEE Trans. Systems, Man and Cybernetics, Vol. 1, No. 9, 1979, pp [1] Seong-Whan Lee and Young Joon Kim, Direct Extraction of Topographic Features for Gray Scale Character Recognition, IEEE Trans. On Pattern Analysis And Machine Intelligence, Vol. 17, No. 7, Jul., [11] Seong-Whan Lee, Dong-June Lee, and Hee-Seon Park, A New Methodology for Gray-Scale Character Segmentation and Recognition, IEEE Trans. On Pattern Analysis And Machine Intelligence, Vol. 18, No. 1, Oct., [12] A. Ariyoshi, A Character Segmentation Method for Japanese Documents Coping with Character Problems, Proc. 31th Int'l Conf. Pattern Recognition, The Hague, Netherlands, Aug., 1992, pp ISSN: ISBN: