Area Location and Recognition of Video Text Based on Depth Learning Method

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1 Vol 21 No 6 JOURNAL OF HARBIN UNIVERSITY OF SCIENCE AND TECHNOLOGY Dec Gabor RBM OCR DOI /j jhust TP A Area Location and Recognition of Video Text Based on Depth Learning Method LIU Ming-zhu 1 ZHENG Yun-fei 1 FAN Jin-fei 1 YU Fang 2 1 School of Measure-control Technology and Communications Engineering Harbin University of Science and Technology Harbin China 2 Dehui Education Technology Service Center of Jilin Province Dehui China Abstract It is advantageous to improve the efficiency and accuracy of video information processing through fast and accurate text area location and recognition of video images The Gabor filter has been used to extract the texture features of video images in the four directions of horizontal vertical left-failing and right-falling Then by RBM layer increment depth learning algorithm a depth belief network has been structured and at the same time the text region location for the texture feature images has been realized The paper also studied the feasibility and recognition effect about using morphological process and OCR character database to realize the video image text recognition The test results showed that the proposed optimized depth learning algorithm combining with morphology character recognition method can not only realize the accurate location of the text region for video images but also improve the efficiency and accuracy of the character recognition Keywords depth learning algorithm video image text area location morphological denoising character recognition 0 OCR lmz@ hrbust edu cn

2 Gabor 3 4 2D-Gabor Daugman 5 OCR 6 OCR Gabor 1 1 exp - 2πj Gabor g x y = Kexp - π p 2 x - x q 2 y - y 0 2 u 0 x - x 0 + v 0 y - y 0 2 F u v = K pq exp - π u - u v - v 0 { 2 p 2 q } 2 1 exp{ - 2πj x 0 u - u 0 + y 0 v - v 0 } Gabor 2 K Gauss x 0 y 0 restricted boltzmann machine RBM Gauss u 0 v 0 p q Gauss x 0 y 0 0 deep belief network 0 p q Gabor DBN 7 - p q 3 8 λ = U h /U I 1 M - 1 p = λ - 1 U h / λ + 1 槡 2ln2 OCR q = tan( π 2T) [ U h - 2ln2 p2 U ] 2ln2-2ln2 2 p [ ] 1 Gabor Gabor U I = U h = 0 4 T = 4 M = b 2 a 4 Gabor 2 h U 2 h 1 3 U h U I T M λ Gabor 4 Gabor 4 4 Wang Gabor Gabor λ λ η 9 Gabor Gabor

Boltzmann E v h θ = - W ij v i h j - b i v i - a j h j 4 ij i j θ = W a b a b RBM θ v P h v = P h j v P h j = 1 v j 1 1 + exp - i W ij - a i I 1 2 L θ = 1 N n N logp θ v n - λ =

3 6 63 b Gabor 3 4 v h 3 RBM 2 Gabor P θ v h = 1 exp - E v Z θ h θ 2 2 = 1 Z θ e W ij v i h j e b i v i e a j h j 5 ij i j Z θ = exp - E v h θ h v 10 depth belief network DBN re- P v h = P v j h P v j = 1 h stricted boltzmann machine RBM i 1 = exp - j W ij h j - b i S n S 1 S 2 S n I O I S 1 S 2 S n O O I I = I S i S i RBM 0 1 P v h Boltzmann v Boltzmann E v h θ = - W ij v i h j - b i v i - a j h j 4 ij i j θ = W a b a b RBM θ v P h v = P h j v P h j = 1 v j exp - i W ij - a i I 1 2 L θ = 1 N n N logp θ v n - λ = 1 N W 2 F 3 L θ W ij L θ W ij 2 3 DBN L θ = E W Pdata v i h j - Pθ E v i h j - 2λ ij N W ij 9 RBM E Pdata v i h j Hinton Sejnowski E Pθ v i h j RBM D = v 1 v 2 v N θ = W a b 8

4 64 21 DBN DBN 2 4 DBN DBN 12 RBM RBM DBN DBN 13 1 H 0 DBN n L 1 L 2 DBN L n DBN H 0 H 0 H 1 W W 0 7 H 1 H 1 W 1 W 2 W n DBN 5 b 5 c 4 DBN DBN W i = W 0 W 1 W 2 Z 2 A W n - 1 DBN n + 2 H 0 H 1 H 2 H n CC A AΘC H 0 AΘC = z C z A n Z 2 C z C z L 1 L 2 L n z DBN H 0 H 1 H 0 H 1 11 Z 2 RBM H 0 v H 1 A C C A A h C W 0 RBM A C = z C z A Φ 11 RBM Z 2 C z C z z Φ RBM DBN 5 5 DBN DBN 4 DBN 5 5

6 65 5 DBN 4 OCR DBN 1 DBN 6 7 OCR 1 DBN OCR 6 5 7 OCR 14 Kim 15 SVM 16 12 RR PR F RR = c m PR = c n 2 PR RR F = PR + RR 12 c m n F 4 1 DBN 4-DBN 4-DBN 4 2 100 4 2 DBN F n m c /%

5 DBN 4 OCR DBN 1 DBN 6 7 OCR 1 DBN OCR OCR 14 Kim 15 SVM RR PR F RR = c m PR = c n 2 PR RR F = PR + RR 12 c m n F 4 1 DBN 4-DBN 4-DBN DBN F n m c /% /% 2-DBN DBN DBN DBN DBN

6 F n m c /% /% Kim SVM DBN DBN 3 Kim SVM F 1 DBN OCR 2 DBN 3 5 Gabor DBN OCR 1 Edge Detection Based on Mathematics Morphology J International Journal of Signal Processing Image Processing and J Pattern 2 EPSHTEIN B OFEK E WEXLER Y Detecting Text in Natural Scenes with Stroke Width Transform C 2010 IEEE Conference on Computer Vision and Pattern Recognition San Francisco USA IEEE Computer Society SVM Natural Scene Images Using Hierarchical Feature Combining and J CHEN X R YUILLE A L Detecting and Reading Text in Natural Scenes C 2004 IEEE Computer Society Conference on Computer Verification C Proceedings of the 17th International Conference on Pattern Recognition Cambridge United Kingdom Institute of Electrical and Electronics Engineers Inc Vision and Pattern Recognition Washington D C USA Institute of Electrical and Electronics Engineers Computer Society YAN J Q LI J GAO X B Chinese Text Location Under Complex Background Using Gabor Filter and SVM J Neurocomputing 5 DAUGMAN J G Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression J IEEE Transactions on Acoustics Speech and Signal Processing J KAMARAINEN Joni KYRKI Ville K LVI INEN Heikki Fundamental Frequency Gabor Filters for Object Recognition J International Conferenceon Pattern Recognition FU P LI M YIN T Gabor Filter Based Text Extraction from Digital Document Image J Tien Tzu Hsueh Pao / Acta Electronica Sinica WANG X W DING X Q LIU C S Optimized Gabor Filter Based Feature Extraction for Character Recognition C 16th International Conference on Pattern Recoqnition Quebec City Canada Institute of Electrical and Electronics Engineers Inc J HINTON G E OSINDERO S BAO K Reducing the Dimensionality of Data with Neural Networks C Proceedings of the 10th International Workshop on Artificial Intelligene and Statistics Barbados Society for Artificial Intelligence and Statistics United States HINTON G E A Practical Guide to Training Restricted Boltzmann Machines J Lecture Notes in Computer Science DENG C X CHEN Y BI H et al Recognition The Improved Algorithm of 14 JUNG K Neural Network-based Text Location in Color Images J Pattern Recognition Letters KIM K C BYUN H R SONG Y J et al Scene Text Extraction in

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