Abstract
Over the last years, the interest in preserving digitally ancient documents has increased resulting in databases with a huge amount of image data. Most of these documents are not transcribed and thus querying operations are limited to basic searching. We propose a novel approach for transcribing historical documents and present results of our initial experiments. Our method divides a text-line image into frames and constructs a graph using the framed image. Then Dijkstra algorithm is applied to find the line transcription. Experiments show a character accuracy of 79.3%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baird, H.S.: The Skew Angle of Printed Documents. In: O’Gorman, L., Kasturi, R. (eds.) Document Image Analysis, pp. 204–208. IEEE Computer Society Press (1995)
Burger, T., Kessentini, Y., Paquet, T.: Dempster-Shafer Based Rejection Strategy for Handwritten Word Recognition. In: 2011 Intl. Conf. on Document Analysis and Recognition (ICDAR 2011), pp. 528–532. IEEE (2011)
Cheriet, M., Kharma, N., Liu, C.L., Suen, C.: Character Recognition Systems – A Guide for Students and Practitioners. Wiley & Sons Inc. (2007)
Cortes, C., Vapnik, V.: Support-Vector Networks. Maching Learning 20(3), 273–297 (1995)
Frinken, V., Bunke, H.: Self-Training for Handwritten Text Line Recognition. In: Bloch, I., Cesar, R.M. (eds.) CIARP 2010. LNCS, vol. 6419, pp. 104–112. Springer, Heidelberg (2010)
Frinken, V., Fischer, A., Bunke, H.: Combining Neural Networks to Improve Performance of Handwritten Keyword Spotting. In: Gayar, N.E., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 215–224. Springer, Heidelberg (2010)
Frinken, V., Fischer, A., Bunke, H., Fornés, A.: Co-training for handwritten word recognition. In: Intl. Conf. on Document Analysis and Recognition (ICDAR 2011), pp. 314–318. IEEE (2011)
Fujisawa, Y., Shi, M., Wakabayashi, T., Kimura, F.: Handwritten Numeral Recognition Using Gradient and Curvature of Gray Scale Image. In: 5th Intl. Conf. on Document Analysis and Recognition (ICDAR 1999), pp. 277–300. IEEE (1999)
Leydier, Y., Lebourgeois, F., Emptoz, H.: Omnilingual Segmentation-freeWord Spotting for Ancient Manuscripts Indexation. In: 8th Intl. Conf. on Document Analysis and Recognition (ICDAR 2005), pp. 533–537 (2005)
Leydier, Y., Lebourgeois, F., Emptoz, H.: Text Search for Medieval Manuscript Images. Pattern Recogntion 40(12), 3552–3567 (2007)
Leydier, Y., Ouji, A., LeBourgeois, F., Emptoz, H.: Towards an Omnilingual Word Retrieval System for Ancient Manuscripts. Pattern Recognition 42(9), 2089–2105 (2009)
Liwicki, M., Bunke, H.: Feature Selection for HMM and BLSTM Based Handwriting Recognition of Whiteboard Notes. International Journal of Pattern Recognition and Artificial Intelligence 23(5), 907–923 (2009)
Rath, T.M., Manmatha, R.: Word Spotting for Historical Documents. International Journal on Document Analysis and Recognition 9(2), 139–152 (2007)
Rath, T.M., Manmatha, R.: Features for Word Spotting in Historical Manuscripts. In: 7th Intl. Conf. on Document Analysis and Recognition (ICDAR 2003), pp. 218–222. IEEE (2003)
Rath, T.M., Manmatha, R.: Word Image Matching Using Dynamic Time Warping. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 521–527 (2003)
Roberts, L.G.: Machine Perception of Three-Dimensional Solids. Ph.D. thesis, Dept. of Electrical Engineering, Massachusetts Institute of Technology (1963)
Romero, V.: Multimodal Interactive Transcription of Handwritten Text Images. Ph.D. thesis, Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia (2010)
Romero, V., Pastor, M.: Computer assisted transcription of text images. In: Toselli, A., Vidal, E., Casacuberta, F. (eds.) Multimodal Interactive Pattern Recognition and Applications, pp. 61–98. Springer (2011)
Romero, V., Rodríguez-Ruiz, L.: Computer assisted transcription: General framework. In: Toselli, A., Vidal, E., Casacuberta, F. (eds.) Multimodal Interactive Pattern Recognition and Applications, pp. 47–60. Springer (2011)
Toselli, A.H., Romero, V., Pastor, M., Vidal, E.: Multimodal Interactive Transcription of Text Images. Pattern Recognition 43(5), 1814–1825 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meza-Lovón, G.L. (2012). A Graph-Based Approach for Transcribing Ancient Documents. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds) Advances in Artificial Intelligence – IBERAMIA 2012. IBERAMIA 2012. Lecture Notes in Computer Science(), vol 7637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34654-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-34654-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34653-8
Online ISBN: 978-3-642-34654-5
eBook Packages: Computer ScienceComputer Science (R0)