Separation of Machine-Printed and Handwritten Texts in Noisy Documents using Wavelet Transform

REFERENCES

• P. Sahare and S. B. Dhok, “Review of text extraction algorithms for scene-text and document images,” IETE Tech. Rev., Vol. 34, no. 2, pp. 144–164, 2017. doi: 10.1080/02564602.2016.1160805
   Web of Science ®Google Scholar
• E. Kavallieratou and S. Stamatatos, “Discrimination of machine-printed from handwritten text using simple structural characteristics,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Cambridge, Aug. 23–6, 2004, Vol. 1, pp. 437–40.
   Google Scholar
• S. Malakar, R. K. Das, R. Sarkar, S. Basu, and M. Nasipuri, “Handwritten and printed word identification using gray-scale feature vector and decision tree classifier,” in International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA), Kalyani, Sept. 27–28, 2013, pp. 831–39.
   Google Scholar
• K. Zagoris, I. Pratikakis, and A. Antonacopoulos, “Distinction between handwritten and machine-printed text based on the bag of visual words model,” Pattern Recogn., Vol. 47, no. 3, pp. 1051–1062, Mar. 2014. doi: 10.1016/j.patcog.2013.09.005
   Web of Science ®Google Scholar
• S. A. Mahmoud and A. S. Mahmoud, “The use of Hartley transform in OCR with application to printed Arabic character recognition,” Pattern Anal. Appl., Vol. 12, pp. 353–365, 2009. doi: 10.1007/s10044-008-0128-8
   Web of Science ®Google Scholar
• Y. Wang and H. Guo, “Shape recognition of tyre marking points based on support vector machine,” IETE Tech. Rev., Vol. 32, no. 2, pp. 123–130, Jan. 2015. doi: 10.1080/02564602.2014.987327
   Web of Science ®Google Scholar
• Y. D. Chun, S. Y. Seo, and N. C. Kim, “Image retrieval using BDIP and BVLC moments,” IEEE Trans. Circuits Syst. Video Technol., Vol. 13, no. 9, pp. 951–957, Sept. 2003. doi: 10.1109/TCSVT.2003.816507
   Web of Science ®Google Scholar
• S. Nigam and A. Khare, “Curvelet transform-based technique for tracking of moving objects,” IET Comput. Vis., Vol. 6, no. 3, pp. 231–251, May 2012. doi: 10.1049/iet-cvi.2011.0023
   Web of Science ®Google Scholar
• X. Q. Luo, Z. C. Zhang, B. C. Zhang, and X. J. Wu, “Contextual information driven multi-modal medical image fusion,” IETE Tech. Rev., Vol. 34, no. 6, pp. 598–611, Oct. 2016.
   Web of Science ®Google Scholar
• P. Nagabhushan, R. Hannane, A. Elboushaki, and M. Javed, “Automatic removal of handwritten annotations from between-text-lines and inside-text-line regions of a printed text document,” in International Conference on Advanced Computing Technologies and Applications (ICACTA), Mumbai, Mar. 26–27, 2015, pp. 205–214.
   Google Scholar
• U. B. Pal and B. B. Chaudhuri, “Machine-printed and hand-written text lines identification,” Pattern Recogn. Lett., Vol. 22, pp. 431–441, Mar. 2001. doi: 10.1016/S0167-8655(00)00126-4
   Web of Science ®Google Scholar
• H. Ding, H. Wu, J. Wang, and X. Zhang, “Handwritten and printed text distinction by using stroke thickness features,” in 7th International Conference on Electronics and Information Engineering, Nanjing, Sept. 17–18, 2016, pp. 103221B-1–103221B-7.
   Google Scholar
• J. Tan, J. H. Lai, P. Wang, and N. Bi, “Multiscale region projection method to discriminate between printed and handwritten text on registration forms,” Int. J. Pattern Recogn. Artif. Intell., Vol. 29, no. 8, pp. 1553005-1–1553005-25, Sept. 2015. doi: 10.1142/S0218001415530055
   Web of Science ®Google Scholar
• M. S. Shirdhonkar and M. B. Kokare, “Discrimination between printed and handwritten text in documents,” IJCA Special Issue Recent Trends Image Process. Pattern Recogn., Vol. 6, no. 3, pp. 131–134, 2010.
   Google Scholar
• K. C. Fan, L. S. Wang, and Y. T. Tu, “Classification of machine-printed and handwritten texts using character block layout variance,” Pattern Recogn., Vol. 31, no. 9, pp. 1275–1284, Sept. 1998. doi: 10.1016/S0031-3203(97)00143-X
   Web of Science ®Google Scholar
• Y. Lin, Y. Song, Y. Li, F. Wang, and K. He, “Multilingual corpus construction based on printed and handwritten character separation,” Multimed. Tools Appl., Vol. 76, no. 3, pp. 4123–4139, Feb. 2017. doi: 10.1007/s11042-015-2995-5
   Web of Science ®Google Scholar
• P. Banerjee and B. B. Chaudhuri, “A system for handwritten and machine-printed text separation in Bangla document images,” in International Conference on Frontiers in Handwriting Recognition, Bari, Sept. 18–20, 2012, pp. 758–762.
   Google Scholar
• M. Kaur, “A technique for classification of printed & handwritten text,” Int. J. Eng. Sci., Vol. 21, pp. 123–128, Dec. 2016.
   Google Scholar
• S. Jang, S. Jeong, and Y. Nam, “Classification of machine-printed and handwritten addresses on Korean mail piece images using geometric features,” in Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, Aug. 2004, pp. 383–386.
   Google Scholar
• Y. Zheng, H. Li, and D. Doermann, “Machine printed text and handwriting identification in noisy document images,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 26, no. 3, pp. 337–353, Mar. 2004. doi: 10.1109/TPAMI.2004.1262324
   PubMed Web of Science ®Google Scholar
• A. Saidani, A. Kacem, and A. Belaid, “Arabic/Latin and machine-printed/handwritten word discrimination using hog-based shape descriptor,” ELCVIA Electron. Lett. Computer Vision Image Anal., Vol. 14, no. 2, pp. 1–23, Dec. 2015. doi: 10.5565/rev/elcvia.762
   Google Scholar
• A. Saïdani, A. Kacem Echi, and A. Belaïd, “Identification of machine-printed and handwritten words in Arabic and Latin Scripts,” in 12th International Conference on Document Analysis and Recognition, Washington, DC, Aug. 25–28, 2013, pp. 798–802.
   Google Scholar
• A. Kacem, A. Saidani, and A. Belaid, “How to separate between machine-printed/handwritten and Arabic/Latin words?,” ELCVIA Electron. Lett. Comput. Vision Image Anal., Vol. 13, no. 1, pp. 1–16, 2014. doi: 10.5565/rev/elcvia.572
   Google Scholar
• J. K. Guo and M. Y. Ma, “Separating handwritten material from machine printed text using Hidden Markov Models,” in Proceeding of 6th International Conference on Document Analysis and Recognition, Seattle, Sept. 13, 2001, pp. 439–443.
   Google Scholar
• R. Srivastava, R. K. Tewari, and S. Kant, “Separation of machine printed and handwritten text for Hindi documents,” Int. Res. J. Eng. Tech., Vol. 2, no. 2, pp. 704–708, May 2015.
   Google Scholar
• U. Patil and M. Begum, “Word level handwritten and printed text separation based on shape features,” Int. J. Emerg Tech. Adv. Eng., Vol. 2, no. 4, pp. 590–594, Apr. 2012.
   Google Scholar
• P. Mathivanan, B. Ganesamoorthy, and P. Maran, “Watershed algorithm based segmentation for handwritten text identification,” ICTACT J. Image Video Process., Vol. 4, no. 3, pp. 767–772, Feb. 2014. doi: 10.21917/ijivp.2014.0111
   Google Scholar
• F. Farooq, K. Sridharan, and V. Govindaraju, “Identifying handwritten text in mixed documents,” in 18th International Conference on Pattern Recognition, Hong Kong, Aug. 20–24, 2006, pp. 1142–1145.
   Google Scholar
• J. E. B. D. Santos, B. Dubuisson, and F. Bortolozzi, “Characterizing and distinguishing text in bank cheque images,” in Proceedings XV Brazilian Symposium on Computer Graphics and Image Processing, Fortaleza-CE, Oct. 7–10, 2002, pp. 203–209.
   Google Scholar
• M. Hangarge, K. C. Santosh, S. Doddamani, and R. Pardeshi, “Statistical texture features based handwritten and printed text classification in south Indian documents,” in Proceedings of International Conference on Emerging Trends in Electrical, Communications and Information Technologies, Tumkur, Nov. 22–23, 2013, pp. 215–221.
   Google Scholar
• L. F. da Silva, A. Conci, and A. Sanchez, “Automatic discrimination between printed and handwritten text in documents,” in XXII Brazilian Symposium on Computer Graphics and Image Processing, Rio de Janeiro, Oct. 11–14, 2009, pp. 261–267.
   Google Scholar
• Y. Ricquebourg, C. Raymond, B. Poirriez, A. Lemaitre, and B. Coüasnon, “Boosting Bonsai trees for handwritten/printed text discrimination,” in SPIE Proceedings 9021, Document Recognition and Retrieval XXI, 902105, California, Feb. 2–6, 2014, pp. 9021-1–9021-12.
   Google Scholar
• X. Peng, S. Setlur, V. Govindaraju, R. Sitaram, and K. Bhuvanagiri, “Markov random field based text identification from annotated machine printed documents,” in 10th International Conference on Document Analysis and Recognition, Barcelona, Jul. 26–29, 2009, pp. 431–435.
   Google Scholar
• X. Peng, S. Setlur, V. Govindaraju, and R. Sitaram, “Handwritten text separation from annotated machine printed documents using Markov random fields”, Int. J. Doc. Anal. Recog., Vol. 16, no. 1, pp. 1–16, 2013. doi: 10.1007/s10032-011-0179-z
   Web of Science ®Google Scholar
• P. Sahare and S. B. Dhok, “Script identification algorithms: a survey”, Int. J. Multimed. Inform. Retr., Vol. 6, no. 3, pp. 211–232, Sept. 2017. doi: 10.1007/s13735-017-0130-2
   Web of Science ®Google Scholar
• S. K. Md. Obaidullah, K. C. Santosh, C. Halder, N. Das, and K. Roy, “Automatic Indic script identification from handwritten documents: page, block, line and word-level approach,” J. Mach. Lear. Cybern., pp. 1–20, Jul. 2017.
   Google Scholar
• D. Lewis, G. Agam, S. Argamon, O. Frieder, D. Grossman, and J. Heard, “Building a test collection for complex document information processing,” in Proceedings of 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Aug. 6–10, 2006, pp. 665–666.
   Google Scholar
• G. Agam, S. Argamon, O. Frieder, D. Grossman, and D. Lewis, “The complex document image processing (CDIP) test collection project,” Illinois Institute of Technology, Chicago, 2006. Available: http://ir.iit.edu/projects/CDIP.html.
   Google Scholar
• “The Legacy Tobacco Document Library (LTDL),” University of California, San Francisco, 2007. Available: http://legacy.library.ucsf.edu/.
   Google Scholar
• M. Pechwitz, S. S. Maddouri, V. Märgner, N. Ellouze, and H. Amiri, “IFN/ENIT-database of handwritten ARABIC words,” in 7th Colloque international Francophone sur l’Ecrit et le document (CIFED), Hammamet, Oct. 21–23, 2002, pp. 129–136.
   Google Scholar
• W. Mao, F. Chung, K. K. M. Lam, and W. Siu, “Hybrid Chinese/English text detection in images and video frames,” in Proceedings of 16th International Conference on Pattern Recognition, Quebec city, Aug. 14, 2002, pp. 1015–1018.
   Google Scholar
• K. P. Soman, K. I. Ramachandran, and N. G. Resmi, Insights into Wavelets from Theory to Practice, 3rd ed. New Delhi: PHI Learning Private Limited, 2011.
   Google Scholar
• S. Li, Q. Shen, and J. Sun, “Skew detection using wavelet decomposition and projection profile analysis,” Pattern Recogn. Lett., Vol. 28, pp. 555–562, Apr. 2007. doi: 10.1016/j.patrec.2006.10.002
   Web of Science ®Google Scholar
• Y. D. Chun, N. C. Kim, and I. H. Jang, “Content-based image retrieval using multiresolution color and texture features,” IEEE Trans. Multimedia, Vol. 10, no. 6, pp. 1073–1084, Oct. 2008. doi: 10.1109/TMM.2008.2001357
   Web of Science ®Google Scholar
• Y. Zheng, H. Li, and D. Doermann, “The segmentation and identification of handwriting in noisy document images,” in International Workshop on Document Analysis Systems (DAS), Princeton, Aug. 19–21, 2002, pp. 95–105.
   Google Scholar
• A. Busch, W. W. Boles, and S. Sridharan, “Texture for script identification,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 27, no. 11, pp. 1720–1732, Nov. 2005. doi: 10.1109/TPAMI.2005.227
   PubMed Web of Science ®Google Scholar
• V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
   Google Scholar
• B. E. Boser, I. Guyon, and V. Vapnik, “A training algorithm for optimal margin classifiers,” in Proceedings of the fifth annual workshop on Computational learning theory (COLT), Pittsburgh, Jul. 27–29, 1992, pp. 144–152.
   Google Scholar
• O. Surinta, M. F. Karaaba, L. R. B. Schomaker, and M. A. Wiering, “Recognition of handwritten characters using local gradient feature descriptors,” Eng. Appl. Artif. Intel., Vol. 45, pp. 405–414, Oct. 2015. doi: 10.1016/j.engappai.2015.07.017
   Web of Science ®Google Scholar
• S. Chanda, S. Pal, and U. Pal, “Word-wise Sinhala Tamil and English script identification using Gaussian kernel SVM,” in 19th International Conference on Pattern Recognition (ICPR), Florida, Dec. 8–11, 2008, pp. 1–4.
   Google Scholar
• V. Vapnik, “An overview of statistical learning theory,” IEEE Trans. Neural Networ., Vol. 10, no. 5, pp. 988–999, Sept. 1999. doi: 10.1109/72.788640
   PubMed Web of Science ®Google Scholar
• C. Cortes and V. Vapnik, “Support vector networks,” Mach. Learn., Vol. 20, no. 3, pp. 197–273, Sept. 1995.
   Web of Science ®Google Scholar
• R. Manmatha and J. L. Rothfeder, “A scale space approach for automatically segmenting words from historical handwritten documents,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 27, no. 8, pp. 1212–1225, Aug. 2005. doi: 10.1109/TPAMI.2005.150
   PubMed Web of Science ®Google Scholar
• “Trails of hope: overland diaries and letters, 1846–1869,” Brigham Young University, Utah. Available: http://overlandtrails.lib.byu.edu.
   Google Scholar
• D. J. Kennard and W. A. Barrett, “Separating lines of text in free-form handwritten historical documents,” in Proceedings of the 2nd International Conference on Document Image Analysis for Libraries, Lyon, Apr. 27–28, 2006, pp. 12–23.
   Google Scholar
• S. Kanoun, I. Moalla, A. Ennaji, and A. M. Alimi, “Script identification for Arabic and Latin, printed and handwritten documents,” in Proceeding of International Workshop on Document Analysis Systems, Brasil, Dec. 10–13, 2000, pp. 159–165.
   Google Scholar
• T. Huang, G. Yang, and G. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Trans. Acoust. Speech Signal Process., Vol. 27, no. 1, pp. 13–18, Feb. 1979. doi: 10.1109/TASSP.1979.1163188
   Google Scholar
• C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Trans. Intell. Syst. Tech., Vol. 2, no. 3, pp. 27:1–27:27, Apr. 2011. doi: 10.1145/1961189.1961199
   Web of Science ®Google Scholar