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Text Extraction and Recognition from the Normal Images using MSER Feature Extraction and Text Segmentation Methods
Image mining is concerned with the extraction of contained information, image information connection or other patterns not clearly stored in the images. Text in images is one of the dominant features and its extraction is a big task. If this type of text could be segmented, detected, extracted and recognized automatically, than it would be a precious source of high-level retrieval process. In the research work, text extraction and recognition from the normal images using MSER feature extraction and text segmentation methods has been developed to detect the text regions and the system is based on efficient optical character recognition process. Text extraction and recognition from the normal images is important for content based image analysis. This problem is challenging due to the complex background of images, reflection of light in images and shadow portion presented in images. The proposed technique in this work develops a well-organized text extraction and recognition methods that utilizes the concept of morphological operations using digital image processing. Existing text extraction method, namely, region based method produces enhanced results when applied on the normal images. The advantage of segmentation for the feature extraction of text region is proposed in the system.
Binarization, Morphological Operations, MSER Feature Extraction, Recognition and Optical Character Recognition, Segmentation, Text Region Detection.
- De Jesus M, Guimaraes SJF, Do Patrocinio Jr ZKG. Video text extraction based on image regularization and temporal analysis. IEEE International Symposium on Multimedia, Dana Point CA: 2011. p. 305–10. CrossRef.
- Patel R, Mitra SK. Extracting text from degraded document image. Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Patna: 2015. p. 1–4. CrossRef.
- Devi GG, Sumathi CP. Text extraction from images using gamma correction method and different text extraction methods — A comparative analysis. International Conference on Information Communication and Embedded Systems (ICICES2014), Chennai: 2014. p. 1–5. CrossRef. PMid:24561215
- Kumuda T, Basavaraj L. Detection and localization of text from natural scene images using texture features. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai: 2015. p. 1–4. CrossRef.
- Zhou Z, XiaowenOu, Xu J. SURF feature detection method used in object tracking. International Conference on Machine Learning and Cybernetics, Tianjin: 2013. p. 1865– 8.
- Juan S, Qingsong X, Jinghua Z. A scene matching algorithm based on SURF feature. International Conference on Image Analysis and Signal Processing, Zhejiang: 2010. p. 434–7.
- Cai P, Wang, Liang YH. Fast image stitching based on improved SURF. IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Nanchang: 2016. p. 411–6.
- Qian S, Chan-juan L, Hai-lin Z, Ying L, Tong-tong C. SURF Feature Description Method of Color Image Based on Quaternion. 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), Krakow: 2015. p. 507–11. CrossRef.
- Harkat H, Elfakir Y, Bennani SD, Khaissidi G, Mrabti M. Ground penetrating radar hyperbola detection using ScaleInvariant Feature Transform. International Conference on Electrical and Information Technologies (ICEIT), Tangiers: 2016. p. 392–7. CrossRef.
- Kabbai L, Azaza A, Abdellaoui M, Douik A. Image matching based on LBP and SIFT descriptor. IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15), Mahdia: 2015. p. 1–6. CrossRef.
- Narhare AD, Molke GV. Trademark Detection Using SIFT Features Matching. International Conference on Computing Communication Control and Automation, Pune: 2015. p. 684–8. CrossRef.
- Borg NP, Debono CJ, Zammit-Mangion D. A single octave SIFT algorithm for image feature extraction in resource limited hardware systems. IEEE Visual Communications and Image Processing Conference, Valletta: 2014. p. 213–6. CrossRef.
- Leutenegger S, Garitachli M, Siegwart RY. BRISK: Binary Robust Invariant Scalable Keypoints. IEEE International Conference on Computer Vision 978-1-4577-11022/11/$26.00 c 2011. IEEE.
- Li, Li H, Söderström U. Scale-invariant corner keypoints. IEEE International Conference on Image Processing (ICIP), Paris: 2014. p. 5741–5.
- Leutenegger S, Chli M, Siegwart RY. BRISK: Binary Robust invariant scalable keypoints. International Conference on Computer Vision, Barcelona, 2011. p. 2548–55. CrossRef.
- Tao L, Jing X, Sun S, Huang H, Chen N, Lu Y. Combining SURF with MSER for image matching. IEEE International Conference on Granular Computing (GrC), Beijing: 2013. p. 286–90. CrossRef.
- Geng Z, Zhuo L, Zhang J, Li X. A comparative study of local feature extraction algorithms for Web pornographic image recognition. 2015 IEEE International Conference on Progress in Informatics and Computing (PIC), Nanjing: 2015. p. 87–92. PMid:26136499 PMCid:PMC4535587
- Kimmel R, Zhang C, Bronstein A, Bronstein M. Are MSER Features Really Interesting?. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011 Nov; 33(11):2316– 20. CrossRef.
- Ali A, Pal R. Detection and extraction of pantograph region from bank cheque images. 3rd International Conference on Signal Processing and Integrated Networks (SPIN), Noida: 2016. p. 498–501. CrossRef.
- Adlinge G, Kashid S, Shinde T, Dhotre VK. Text Extraction from image using MSER approach. International Research Journal of Engineering and Technology (IRJET), 2016. p. 2453–7.
- Mammeri A, Boukerche A, Khiari EH. MSER-based text detection and communication algorithm for autonomous vehicles. IEEE Symposium on Computers and Communication (ISCC), Messina: 2016. p. 1218–23. CrossRef.
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