Review of summarization on chart data

  • Authors

    • Aditi Deshpande Dr. Babasaheb University
    • Deepali Sawane Dr.babasaheb university
    • Sanah Sayyed
    • Namrata Mahender
    2018-11-15
    https://doi.org/10.14419/ijet.v7i4.20870
  • Graph Summarization, OCR, Automatic Graph Summarization, Types, Stepwise Methodology.

  • Abstract

    To summarize documents worths to summation of the main points. A summarization is this kind of summing up. Elementary school book reports are big on summarization. To provide a comprehensible declaration of the significant points is nothing but summarization. In current years, natural language processing (NLP) has stimulated to statistical base. Many tribulations in NLP, e.g., parsing, word sense disambiguation, and involuntary paraphrasing. In recent times, robust graph-based methods for NLP is also a lot of scope, e.g., in clustering of words and attachments of prepositional phrase. In proposed paper, we will take in account of graph-based summarization techniques, approaches used for that etc. We will talk about how arbitrary traversing on images of graphs can help in making of question answer based summarization. In current exploration work, the extraction procedure is completely computerized using image processing and text recognition methods i.e. done with the help of OCR. The extracted information can be used to improve the indexing component for bar charts and get better exploration results. After generating questions, questions are rank the according to frequency or priority and answer of the ranked question is summary of given input.

     

     

     


  • References

    1. [1] Casey, R., Lecolinet, E.: A survey of methods and strategies in character segmentation. IEEE Trans. Pattern Anal. Mach. Intell.18 (7), 690–706 (2002) https://doi.org/10.1109/34.506792.

      [2] Chen, D., Odobez, J., Bourlard, H.: Text detection and recognition in images and video frames. Pattern Recogn.37 (3), 595–608 (2004) https://doi.org/10.1016/j.patcog.2003.06.001.

      [3] Chen, T., Ghosh, D., Ranganath, S.: Videotext extraction and recognition. In: IEEE Region 10 Conference, TENCON’04, vol.1, pp. 319–322 (2005)

      [4] K. Jung, K. I. Kim, and Anil. K. Jain, ―Text information extraction in images and video: a survey‖, Pattern Recognition, vol. 37, 2004, pp.977-997. https://doi.org/10.1016/j.patcog.2003.10.012.

      [5] J. Zhang and R. Kasturi, ―Extraction of Text Objects in Video Documents: Recent Progress‖, DAS, 2008, pp.5-17.

      [6] Yao C, Zhang X, Bai X, Liu W, Tu Z. Rotation-invariant features for multi-oriented text detection in natural images. PloS one, 2013, 8(8):e70173 International Journal of Computer Applications (0975 – 8887) Volume 127 – No.15, October 2015

      [7] Yao C, Bai X, Shi B, LiuW. Strokelets: A learned multi-scale representation for scene text recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2014, 4042–4049 https://doi.org/10.1109/CVPR.2014.515.

      [8] Delakis, M., Garcia, C.: Text detection with convolutional neural networks. In: International Conference on Computer Vision Theory and Applications, vol. 2, pp. 290–294 (2008)

      [9] ABBYY Fine Reader.http://finereader.abbyy.com/.

      [10] Microsoft Office Document Imaging. http://en.wikipe_dia.org/wiki/Microsoft_Office_Document_Imaging

      [11] M. Cai, J. Song and M.R. Lyu, ‖A New Approach for Video Text Detection,‖ in Proc. IEEE Int’l Conf. Image Processing, pp.117-120, 2002.

      [12] Yao C, Bai X, Liu W, Ma Y, Tu Z. Detecting texts of arbitrary orientations in natural images. In: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1083–1090

      [13] C. Jung, Q. Liu, and J. Kim, ―A new approach for text segmentation using a stroke filter‖, Signal Processing, vol.88, 2008, pp.1907-1916. https://doi.org/10.1016/j.sigpro.2008.02.002.

      [14] X. Li, W. Wang, S. Jiang, Q. Huang, and Wen Gao, ―Fast and Effective text detection‖, ICIP, 2008, pp.969-972. https://doi.org/10.1109/ICIP.2008.4711918.

      [15] C. Jung, Q. Liu, and J. Kim, ―A stroke filter and its application to text localization‖, Pattern Recognition Letters, vol.30, 2009, pp.114-122. https://doi.org/10.1016/j.patrec.2008.05.014.

      [16] B. Epshtien, E. Ofek, Y. Wexler, ―Detecting text in natural scenes with Stroke Width Transform‖, CVPR, 2010, pp. 2963 - 2970. https://doi.org/10.1109/CVPR.2010.5540041.

      [17] P. Shiva kumara, W. Huang, C. L. Tan, ―An Efficient Edge Based Technique for Text Detection in Video Frames‖, DAS, 2008, pp.307-314.

      [18] Hua, X., Yin, P., Zhang, H.: Efficient video text recognition using multiple frame integration. In: International Conference on Image Processing, vol. 2, pp. 397–400 (2002)

      [19] Li, H., Doermann, D., Kia, O.: Automatic text detection and tracking in digital video. IEEE Trans. Image Process.9 (1), 147–156(2000) https://doi.org/10.1109/83.817607.

      [20] Yi C, Tian Y L. Text string detection from natural scenes by structure based partition and grouping. IEEE Transactions on Image Processing, 2011, 20(9): 2594–2605 https://doi.org/10.1109/TIP.2011.2126586.

      [21] J. Park, G. Lee, E. Kim, J. Lim, S. Kim, H. Yang, M. Lee, and S.Hwang, ―Automatic detection and recognition of Korean text in outdoor signboard Images‖, Pattern Recognition Letters, vol.31,2010, pp.1728-1739. https://doi.org/10.1016/j.patrec.2010.05.024.

      [22] P. Shiva kumara, W. Huang, T. Q. Phan, C. L. Tan, ― Accurate videotext detection through classification of low and high contrast Images‖, Pattern Recognition, vol.43, 2010, pp.2165-2185. https://doi.org/10.1016/j.patcog.2010.01.009.

      [23] P. Shiva kumara, A. Dutta, U. Pal, and C. L. Tan, ―A New method for Handwritten scene text detection in video‖, ICFHR, 2010, pp.387-392

      [24] J. Yu and Y. Wang, ―Apply SOM to Video Artificial text area detection‖, Int. Conf. Internet computing for Science. And Engg, 2010, pp.137-141.

      [25] X. Huang and H. Ma, ―Automatic Detection and Localization of Natural scene text in Video‖, ICPR, 2010, pp.3216-3219. https://doi.org/10.1109/ICPR.2010.786.

      [26] Li H, Doermann D, Kia O. Automatic text detection and tracking in digital video. IEEE Transactions on Image Processing, 2000, 9(1):147–156 https://doi.org/10.1109/83.817607.

      [27] M.R. Lyu, J. Song, and M. Cai, ‖A Comprehensive Method for Multilingual Video Text Detection, Localization, and Extraction, ‖IEEE Trans. Circuits System on Video Technology, vol. 15,no. 2, pp. 243-255, 2005. https://doi.org/10.1109/TCSVT.2004.841653.

      [28] Yi, J., Peng, Y., Xiao, J.: Using multiple frame integration for the text recognition of video. In: International Conference on Document Analysis and Recognition, pp. 71–75 (2009) https://doi.org/10.1109/ICDAR.2009.58.

      [29] Yokobayashi, M., Wakahara, T.: Segmentation and recognition of characters in scene images using selective binarization in color space and GAT correlation. In: International Conference on Document Analysis and Recognition, pp. 167–171 (2005)

      [30] Shivakumara P, Phan T Q, Tan C L. A laplacian approach to multioriented text detection in video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2): 412–419 https://doi.org/10.1109/TPAMI.2010.166.

      [31] Z. Saidane and C. Gracia, ―Automatic Scene Text Recognition using a Convolutional Neural Network‖, CBDAR, 2007, pp.100-107.

      [32] A. Ohkura, D. Deguchi, T. Takahashi, I. Ide, and H. Murasse, ―Low resolution Character Recognition by Video-based Super-resolution‖, ICDAR, 2009, pp.191-19.

      [33] Z. Saidane, C. Garcia, and J. L. Dugelay, ―The image Text Recognition Graph (iTRG) ‖, ICME, 2009, pp.266-269 https://doi.org/10.1109/ICME.2009.5202486.

      [34] P. Shiva kumara, W. Huang and C.L. Tan, ‖Efficient Video Text Detection using Edge Features,‖ in Proc. IEEE Int’l Conf. Pattern Recognition, pp. 1-4, 2008.

      [35] Everingham M, Van Gool L, and Williams C K I, Winn J, Zisserman A. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 2010, 88(2): 303–338 https://doi.org/10.1007/s11263-009-0275-4.

      [36] Z. Zhou, L. Li, C. L. Tan, ―Edge based Binarization for video text images‖, ICPR, 2010, pp.133-136.

      [37] A. Mishra, K Alahari, and C. V. Jawahar, ―An MRF Model for Binarization of Natural Scene Text‖, ICDAR, 2011, pp-11-16.

      [38] Toru Wakahara and Kohei Kita, ―Binarization of Color Character Strings in Scene Images Using K-Means Clustering and Support Vector Machines‖, ICDAR, 2011, pp.274-278.

      [39] K. Ntirogiannis, B. Gatos, and I. Pratikakis ―Binarization of Textual Content in Video Frames‖, ICDAR, 2011, pp.673-677.

      [40] W. Wu, D. Chen, and J. Yang, ―Integrating co-training and recognition for text detection,‖ in Proc. IEEE Int. Conf. Multimedia Expo, 2005, pp. 1166–1169.

      [41] T. Yamazoe, M. Etoh, T. Yoshimura, and K. Tsujino, ―Hypothesis preservation approach to scene text recognition with weighted finite-state transducer, ‖ in Proc. Int. Conf. Doc. Anal. Recognit. 2011, pp. 359–363.

      [42] K. Wang, B. Babenko, and S. Belongie, End-to-end scene text recognition, ‖ in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 1457– 1464.

      [43] T. Wang, D. J. Wu, A. Coates, and A. Y. Ng,End-to-end text recognition with convolution neural networks,‖ in Proc. IEEE Int. Conf. Pattern Recognit., 2012, pp. 3304–3308.

      [44] L. Neumann and J. Matas, On combining multiple segmentations in scene text recognition,‖ in Proc. IEEE Int. Conf. Doc. Anal. Recognit., 2013, pp. 523–527.

      [45] K. Wang and S. Belongie, ―Word spotting in the wild,‖ in Proc. Eur. Conf. Comput. Vis., 2010, pp. 591–604.

      [46] P. Shivakumara, W. Huang, and C. L. Tan, ―Efficient video text detection using edge features,‖ in Proc. IEEE Int. Conf. Pattern Recognit., 2008 pp. 1–4.

      [47] M. R. Lyu, J. Song, and M. Cai, ―A comprehensive method for multilingual video text detection, localization, and extraction,‖ IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 2, pp. 243– 255, Feb. 2005. https://doi.org/10.1109/TCSVT.2004.841653.

      [48] M. Cai, J. Song and M. R. Lyu, ―A new approach for video text detection,‖ in Proc. IEEE Int. Conf. Image Process., 2002, pp. 117– 120.

      [49] V. I. Levenshtein, ―Binary codes capable of correcting deletions, insertions, and reversals,‖ Soviet Phys. Doklady, vol. 10, no. 8, pp. 707–710, 1966.

      [50] A Unified Framework for Multioriented Text Detection and Recognition‖ IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 11, NOVEMBER 2014, 4737

      [51] Robust Text Detection in Natural Scene Images‖ IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 36, NO. 5, MAY 2014

      [52] Scene Text Recognition Using Structure-Guided Character Detection and Linguistic Knowledge‖ IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 24, NO. 7, JULY 2014 1235

      [53] A Hybrid Approach to Detect and Localize Texts in Natural Scene Images Yi-Feng Pan, Xinwen Hou, and Cheng-Lin Liu, Senior Member, IEEE, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 3, MARCH 2011

      [54] Yogan J. and Ong S.-Survey of Region-Based Text Extraction Techniques for Efficient Indexing of Image/Video Retrieval I.J. Image, Graphics and Signal Processing, 2014

      [55] A Survey on various approaches of text extraction in Images C.P. Sumathi, T. Santhanam and G.Gayathri Devi, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.4, August 2012.

      [56] Chu Duc Nguyen, Mohsen Ardabilian and Liming Chen ,‖Robust Car License Plate Localization using a Novel Texture Descriptor‖, Advanced Video and SignalBased Surveillance, 2009 16.

      [57] Edge Detection Techniques for Image Segmentation, Muthukrishnan.R and M.Radha, nternational Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 6, Dec 2011

      [58] Garg Payal and Singh Bedi Er. Charndeep. 2014. A review on Question Generation System Form Punjabi Text. International Journal of Emerging Trends and Technology in Computer Science (ITETTCS).

      [59] GargShikha and Goyal Vishal. 2013. System for Generating Questions Automatically From Given Punjabi Text. International Journal of Computer Science and Mobile Computing, 324-327.

      [60] GovilkarSharvari.Bakal J. W and RathodShubhangi. 2015. Part of Speech Tagger for Marathi Language. International Journal of Computer Applications.Volume 119 – No.18.

      [61] Gupta Vishal. 2013. A Survey of Text Summarizers for Indian Languages and Comparison of their Performance. Journal of Emerging Technologies In Web Intelligence, Vol. 5, No. 4

      [62] Gupta Vishal and LehalGurpreet Singh. 2011. Named entity recognition for Punjabi language text summarization. International journal of computer applications. pp. 28 - 32.

      [63] KaurJaspreet and Bathla Ashok Kumar. 2015. A Review on Automatic Question Generation System from a given Hindi Text. International Journal of Research in Computer Application and Robotics. Vol. 3 Issue 6.pp 87-92.

      [64] KaurRamandeep and BansalShilpy. 2014. A Review on various Techniques for Automatic Question Generation. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS).Vol. 3.Issue 6.

      [65] Khillare Sunil Arun. 2016. Development of Question Answering System in Devnagari Script: In Context of Examination (Doctoral Dissertation). Ph. D. Thesis. Dr.BabasahebAmbedkarMarathwada University, Aurangabad. India.

      [66] ManneSuneetha, ShaikMohdZaheerParvez. and S. Sameen Fatima. 2012. Extraction Based Automatic Text Summarization System with HMM Tagger. Proceedings of the International Conference on Information Systems Design and Intelligent Applications.Vol. 132. pp 421-428.

      [67] MannemPrashanth.Prasad Rashmi and Joshi Aravind. 2010. Question generation from paragraphs at UPenn: QGSTEC system description. International institute of information technology. pp. 1-8.

      [68] Saranyamol C S and Sindhu L. 2014.A Survey on Automatic Text Summarization.International Journal of Computer Science and Information Technologies.Vol. 5 Issue 6.

      [69] Singh Parshan and KaurRajbhupinder. 2014. .Rule based question generation system from Punjabi text contain historical information. International journal of computer science and mobile computing. pp. 86-91.

      [70] Singh Jasmeet and Gupta Vishal. 2016. A Systematic Review of Text Stemming Techniques. ArtifIntell Rev Springer Science Business Media Dordrecht.

      [71] MajgaonkerMudassar M. And SiddiquiTanveer J. 2010.Discovering Suffixes: A case Study for Marathi Language. International Journal on Computer Science and Engineering.Vol. 02. No. 08.

      [72] Renjith S R. 2015. A Survey on Sentence Similarity Based Automatic Text Summarization Techniques. International Journal of Advanced Computing and Electronics Technology (IJACET). Volume-2, Issue-3.

  • Downloads

  • How to Cite

    Deshpande, A., Sawane, D., Sayyed, S., & Mahender, N. (2018). Review of summarization on chart data. International Journal of Engineering & Technology, 7(4), 4544-4549. https://doi.org/10.14419/ijet.v7i4.20870