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Evaluation of Commercial OCR: A New Goal Directed Methodology for Video Documents

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Pattern Recognition and Data Mining (ICAPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3686))

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Abstract

Texts embedded in video streams convey crucial information for documentation. Many text detection and recognition systems have been designed to automatically extract such documentary data from video streams. Most of the research teams involved argue that commercial OCR do not work properly on images extracted from a video stream. They thus concieve their own detection systems. Nevertheless, commercial OCR have never been evaluated on such corpora. This article details a new methodology to evaluate a commercial OCR on a video document. This methodology is goal directed: the system is penalized proportionally to TFIDF (Term Frequency Inverse Document Frequency) scores of texts [1]. We experiment our methodology on Abbyy FineReader 6.0.

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References

  1. Jones, K.S., Walker, S., Robertson, S.: A probabilistic model of information retrieval: development and status. Technical Report Technical Report 446, University of Cambridge Computer Laboratory (1998)

    Google Scholar 

  2. Wu, V., Manmatha, R., Riseman, E.: Textfinder: An automatic system to detect and recognize text in images. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 1224–1229 (1999)

    Article  Google Scholar 

  3. Wolf, C., Jolion, J.-M.: Extraction and recognition of artificial text in multimedia documents. Pattern Analysis and Applications 6, 309–326 (2003)

    MathSciNet  Google Scholar 

  4. Li, H., Doermann, D.: Automatic text detection and tracking in digital video. IEEE Transactions on Image Processing 9, 147–156 (2000)

    Article  Google Scholar 

  5. Chen, D., Odobez, J.M., Boulard, H.: Text detection and recognition in images and video frames. Pattern Recognition 37, 595–608 (2004)

    Article  Google Scholar 

  6. Wolf, C.: Détection de textes dans des images issues d’un flux vidéo pour l’indexation sémantique. PhD thesis, Institut National de Sciences Appliquées de Lyon, France (2003)

    Google Scholar 

  7. Li, H.: Automatic processing and analysis of text in digital video. PhD thesis, University of Maryland, College Park (2000)

    Google Scholar 

  8. Hua, X.S., Wenyin, L., Zhang, H.J.: An automatic performance evaluation protocol for video text detection algorithms. IEEE Trans. on Circuits and Systems for Video Technology 14, 498–507 (2004)

    Article  Google Scholar 

  9. Doermann, D., Mihalcik, D.: Tools and techniques for video performance evaluation. In: Proceedings of the ICPR 2000, vol. 4, pp. 4167–4170. IEEE Computer Society, Los Alamitos (2000)

    Google Scholar 

  10. Yanikoglu, B., Vincent, L.: Pink panther: A complete environment for ground-truthing and benchmarking document page segmentation. Pattern Recognition 31, 1191–1204 (1998)

    Article  Google Scholar 

  11. Lee, C.H., Kanungo, T.: The architecture of trueviz: a groundtruth/metadata editing and visualizing toolkit. Pattern Recognition 36, 811–825 (2003)

    Article  Google Scholar 

  12. Fruchterman, T.: Dafs: A standard for document and image understanding. In: Proceedings of the Symposium on Document Image Understanding Technology, pp. 94–100 (1995)

    Google Scholar 

  13. Liang, J., Philips, I., Haralick, R.: Performance evaluation of document layout analysis algorithms on the uw data set. In: Document Recognition IV, Proceedings of the SPIE, pp. 149–160 (1996)

    Google Scholar 

  14. Lienhart, R., Wernike, A.: Localizing and segmenting text in images, videos and web pages. IEEE Transactions on Circuits and Systems for Video Technology 12, 256–268 (2002)

    Article  Google Scholar 

  15. Mariano, V.Y., Min, J., Park, J.H., Kasturi, R., Mihalcik, D., Li, H., Doermann, D.: Performance evaluation of object detection algorithms. In: International Conference on Pattern Recognition (2002)

    Google Scholar 

  16. Mao, S., Kanungo, T.: Empirical performance evaluation of page segmentation algorithms. In: Proceedingsof SPIE Conference on Document Recognition, San Jose CA (2000)

    Google Scholar 

  17. Kanai, J., Rice, S., Natker, T., Nagy, G.: Automated evaluation of ocr zoning. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 86–90 (1995)

    Article  Google Scholar 

  18. Wagner, R., Fisher, M.J.: The string to string correction problem. Journal of Assoc. Comp. Mach. 21, 168–173 (1974)

    MATH  Google Scholar 

  19. Van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Dept. of Computer Science. University of Glasgow (1979)

    Google Scholar 

  20. Jolion, J.: The deviation of a set strings. Pattern Analysis And Application 6, 224–231 (2004)

    Article  Google Scholar 

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Landais, R., Vinet, L., Jolion, JM. (2005). Evaluation of Commercial OCR: A New Goal Directed Methodology for Video Documents. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_74

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  • DOI: https://doi.org/10.1007/11551188_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28757-5

  • Online ISBN: 978-3-540-28758-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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