Robust Feature Extraction Method Based on Run-Length Compensation for Degraded Character Recognition

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1 Robust Feature Extraction Method Based on Run-Length Compensation for Degraded Character Recognition Minoru MORI, Minako SAWAKI, Norihiro HAGITA, Hiroshi MURASE, and Naoki MUKAWA OCR 1. [1] [4] [5] [7] NTT NTT Communication Science Laboratories, NTT Corporation, 3 1 Morinosato-Wakamiya, Atsugi-shi, Japan ATR [8] [9] LSD [10], [11] [12] [14] [15] 1 1 D II Vol. J86 D II No. 7 pp

2 2003/7 Vol. J86 D II No ETL-9 [16] G W W I 1 (x, y) g x,y x y g x,y a b c e a = b = c = e = I 2 k=0 I 2 k=0 I 2 k=0 I 2 k=0 g x+k,y g x+k+1,y (1) (1 g x+k,y ) g x+k+1,y (2) g x+k,y (1 g x+k+1,y ) (3) (1 g x+k,y ) (1 g x+k+1,y ) (4) a b c e r o r t r n r o = r t + r n (5) r t Rectangular window Black White Scanning I pixels Enlarged part Transition between black and white pixels 1 Fig. 1 Extraction of parameters from the input image. 1050

3 r t = r o r n =(1 r n/r o) r o (6) r n/r o < = r n/r o < = 1 (7) r n/r o 2. 1 a b c e r n/r o r n/r o K add V add β add β add = K add / V add (8) β add 1 K add V add β add 0 r n/r o 1 β add (9) (9) (6) r t =(1 (1 β add )) r o = β add r o (10) β add K add V add a b c e ā b c ē K add =(a + b)/(ā + b) (11) V add =(b + c)/( b + c) (12) r o r o = a + b (13) r t (8) (10) (13) r t = (a + b)/(ā + b) (a + b) (14) (b + c)/( b + c) 2. 3 r o r t r n r o = r t r n (15) r t r t = r o + r n =(1+r n/r o) r o. (16) K sub V sub β sub β sub = K sub / V sub (17) K sub =(e + c)/(ē + c) (18) V sub =(b + c)/( b + c) (19) r n/r o 1 β sub (20) r t r t = ( 2 ) (e + c)/(ē + c) (a + b) (21) (b + c)/( b + c) 1051

4 2003/7 Vol. J86 D II No. 7 rt = 8 (a) Noise-free rt = 2 & 9 (b) Additive noise r't = 6.9 r't = (I = W ) a b c e p(y) (y =1,...,W) [6] p(y) = a e b c (a + b) (c + e) (a + c) (b + e) ( 1 < = p(y) < = 1) (22) rt = 1 & 4 (c) Subtractive noise r't = ā =7.4 b =0.9 c =0.9 ē =4.8 Fig. 2 Extraction of observed run-length and compensatedrun-length. ā = 7.4, b = 0.9, c = 0.9, and ē = 4.8 are calculated from training data r t r t =8 r t =2&9 r t =1&4 r t =6.1 r t = a e b c a e b c 2W T 2W T M T M M 4. [9] LSD [10], [11] d i (i =1,...,4) d i = l i 4 j=1 lj 2 (23) l 1 l 2 l 3 l 4 3 Step 1: 1052

5 Fig. 3 N blocks N blocks Partitioning Enlarged part l4 1 Scanning direction l3 l1 Run-length extraction 3 Feature extraction of direction contributivity. l2 Step 5: (23) l i r t,i d i 1 N =8 8 8=64 4 (a) 4 (b) 4 5. Horizontal Vertical (a) Feature values based on compensated run-length Horizontal Vertical (b) Feature values based on observed run-length 4 Fig. 4 Examples of the feature values based on compensated run-length and observed run-length. Horizontal and vertical feature values are visualized. Step 2: N N Step 3: Step 1 r t,i (i =1,...,4) Step 4: r t,i 5. 1 ETL-9 3, /[16] (1,...,199) 100 / (2,...,200) 100 / [6] α (α <0) α (α > = 0) α 70 < = α < = 70[ ] 10 G α G Z α AND G α G Z α OR 1053

6 2003/7 Vol. J86 D II No. 7 g α x,y = { g x,y zx,y α g x,y zx,y α if α<0, otherwise. (24) gx,y, α g x,y, zx,y α G α, G, Z α (x, y) [6] W = = I 1 Noise model (a) Additive noisy image Noise model (b) Subtractive noisy image Noisy character Noisy character 5 Fig. 5 Additive noise model, subtractive noise model, and each noisy character image. b c 1 1 I =15 ±7 I =15 I =11 ±5 ā b c ē < = α < = α , seed 1 10 < = α < = α =0 4.1 α = α 1 [ ] Table 1 Noise type detection accuracy [ ]. Input Output Subtractive Additive Subtractive Additive

7 Feature based on compensated run Median filter + Feature based on observed run Feature based on observed run 100 Noise: 1 1 Noise: 2 2 Noise: 3 3 Noise: 4 4 Noise: 5 5 Recognition rate [%] Noise Recognition rate [%] Noise -60% -30% 0% 30% 60% 6 Fig. 6 Recognition rates for patterns with subtractive or additive noise < = α < = α > = Fig. 7 Recognition rates for patterns with each size of subtractive/additive noise α < = 60 α > = 60 α < = 60 2 (21) < = α < =

8 2003/7 Vol. J86 D II No. 7 α = α =0 0 α = 10 0 α (a) 9 (b) [17] [19] 8 Fig. 8 Examples of images recognized correctly by the proposed method. (a) 9 Fig. 9 Examples of images recognized erroneously by the proposed method (Correct category Erroneous category as the recognition result). (b) ETL-9 ETL-9 [1] G.E. Kopec, Supervised template estimation for document image decoding, IEEE Trans. Pattern Anal. Mach. Intell., vol.19, no.12, pp , Dec [2] Y. Xu and G. Nagy, Prototype extraction and adaptive OCR, IEEE Trans. Pattern Anal. Mach. Intell., vol.21, no.12, pp , Dec [3] T.K. Ho, Bootstrapping text recognition from stop words, Proc. 14th ICPR, vol.1, pp , Brisnane, Australia, Aug [4] M. Sawaki, H. Murase, and N. Hagita, Automatic acquisition of context-based images templates for degraded character recognition in scene images, Proc. 15th ICPR, pp.15 18, Barcelona, Spain, Sept [5] [6] M. Sawaki and N. Hagita, Text-line extraction and 1056

9 character recognition of document headlines with graphical designs using complementary similarity measure, IEEE Trans. Pattern Anal. Mach. Intell., vol.20, no.10, pp , Oct [7] A. Sato, A learning method for definite canonicalization based on minimum classification error, Proc. 15th ICPR, vol.2, pp , Barcelona, Spain, Sept [8] M. Umeda, Advances in recognition methods for handwritten Kanji characters, IEICE Trans. Inf. & Syst., vol.e79-d, no.5, pp , May [9] D vol.j66-d, no.10, pp , Oct [10] J. Zhu, T. Hong, and J.J. Hull, Image-based keyword recognition in oriental language document images, Pattern Recognit., vol.30, no.8, pp , Aug [11] S.N. Srihari, T. Hong, and G. Srikantan, Machineprinted Japanese document recognition, Pattern Recognit., vol.30, no.8, pp , Aug [12] H. Ozawa and T. Nakagawa, A character image enhancement method from characters with various background image, Proc. 2nd ICDAR, pp.58 61, Oct [13] S. Liang, M. Ahmadi, and M. Shridhar, A morphological approach to text string extraction from regular periodic overlapping text/background images, CVGIP, vol.56, no.5, pp , Sept [14] M.Y. Yoon, S.W. Lee, and J.S. Kim, Faxed image restoration using Kalman filtering, Proc. 3rd ICDAR, vol.2, pp , Montreal, Canada, Aug [15] Z. Shi and V. Govindaraju, Character image enhancement by selective region-growing, Pattern Recognit. Lett., vol.17, no.5, pp , May [16] JIS 1 ETL9 D vol.j68-d, no.4, pp , April [17] D-II vol.j79-d-ii, no.9, pp , Sept [18] S. Omachi, M. Inoue, and H. Aso, A noise-adaptive discriminant function and its application to blurred machine-printed Kanji recognition, IEEE Trans. Pattern Anal. Mach. Intell., vol.22, no.3, pp , March [19] D-II vol.j83-d-ii, no.7, pp , July NTT 11 NTT NTT ATR 56 IEEE NTT 4 1 NTT CVPR 7 8 ICRA IEEE NTT 6 12NTT 12 NTT IEEE 1057

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