A combined system for text line extraction and handwriting recognition in historical documents

Proceedings - 11th IAPR International Workshop on Document Analysis Systems, DAS 2014

Volumn , Issue , 2014, Pages 71-75

  Fischer, Andreas ^a   Baechler, Micheal ^b   Garz, Angelika ^b   Liwicki, Marcus ^b   Ingold, Rolf ^b  

^a ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

^b UNIVERSITY OF FRIBOURG   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

DIGITAL LIBRARIES;

DOCUMENT ANALYSIS; EVALUATION MEASURES; EXPERIMENTAL EVALUATION; HANDWRITING RECOGNITION; HISTORICAL DOCUMENTS; LEARNING-BASED METHODS; TEXT LOCALIZATION; TEXT-LINE EXTRACTIONS;

EXTRACTION;

EID: 84903711003     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DAS.2014.51     Document Type: Conference Paper

References (29)

1. R. Manmatha and J. L. Rothfeder, "A scale space approach for automatically segmenting words from historical handwritten documents," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1212-1225, 2005.
2. L. Likforman-Sulem, A. Zahour, and B. Taconet, "Text line segmentation of historical documents: A survey," Int. Journal on Document Analysis and Recognition, vol. 9, no. 2, pp. 123-138, 2007.
3. Z. Shi and V. Govindaraju, "Line separation for complex document images using fuzzy runlength," in Proc. Int. Conf on Document Image Analysis for Libraries, 2006, pp. 306-312.
4. N. Nikolaou, M. Makridis, B. Gatos, N. Stamatopoulos, and N. Papamarkos, "Segmentation of historical machine-printed documents using adaptive run length smoothing and skeleton segmentation paths," Image and Vision Computing, vol. 28, no. 4, pp. 590-604, 2010.
5. Z. Shi, S. Setlur, and V. Govindaraju, "Text extraction from gray scale historical document images using adaptive local connectivity map," in Proc. Int. Conf. on Document Analysis and Recognition, 2005, pp. 794-798.
6. I. Bar-Yosef, N. Hagbi, K. Kedem, and I. Dinstein, "Text line segmentation for degraded handwritten historical documents," in Proc. Int. Conf. on Document Analysis and Recognition, 2009, pp. 1161-1165.
7. L. Likforman-Sulem, A. Hanimyan, and C. Faure, "A Hough based algorithm for extracting text lines in handwritten documents," in Proc. Int. Conf. on Document Analysis and Recognition, 1995, pp. 774-777.
8. G. Louloudis, B. Gatos, I. Pratikakis, and C. Halatsis, "Text line detection in handwritten documents," Pattern Recognition, vol. 41, no. 12, pp. 3758-3772, 2008.
9. M. Liwicki, E. Indermühle, and H. Bunke, "Online handwritten text line detection using dynamic programming," in Proc. Int. Conf. on Document Analysis and Recognition, 2007, pp. 447-451.
10. A. Asi, R. Saabni, and J. El-Sana, "Text line segmentation for gray scale historical document images," in Proc. Int. Workshop on Historical Document Imaging and Processing, 2011, pp. 120-125.
11. Y. Li, Y. Zheng, D. Doermann, and S. Jaeger, "Script-independent text line segmentation in freestyle handwritten documents," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1313-1329, 2008.
12. J. Edwards, Y. W. Teh, D. Forsyth, R. Bock, M. Maire, and G. Vesom, "Making Latin manuscripts searchable using gHMM's," in Advances in Neural Information Processing Systems, vol. 17, 2005, pp. 385-392.
13. N. R. Howe, S. Feng, and R. Manmatha, "Finding words in alphabet soup: Inference on freeform character recognition for historical scripts," Pattern Recognition, vol. 42, no. 12, pp. 3338-3347, 2009.
14. A. H. Toselli, V. Romero, M. Pastor, and E. Vidal, "Multimodal interactive transcription of text images," Pattern Recognition, vol. 43, no. 5, pp. 1814-1825, 2010.
15. F. L. Bourgeois and H. Emptoz, "DEBORA: Digital AccEss to BOoks of the RenAissance," Int. Journal on Document Analysis and Recognition, vol. 9, pp. 193-221, 2007.
16. B. Coüasnon, J. Camillerapp, and I. Leplumey, "Access by content to handwritten archive documents: Generic document recognition method and platform for annotations," Int. Journal on Document Analysis and Recognition, vol. 9, no. 2, pp. 223-242, 2007.
17. B. Gatos, N. Stamatopoulos, and G. Louloudis, "ICDAR2009 handwriting segmentation contest," Int. Journal on Document Analysis and Recognition, vol. 14, no. 1, pp. 25-33, 2011.
18. A. Fischer, E. Indermühle, H. Bunke, G. Viehhauser, and M. Stolz, "Ground truth creation for handwriting recognition in historical documents," in Proc. Int. Workshop on Document Analysis Systems, 2010, pp. 3-10.
19. N. Naji and J. Savoy, "Information retrieval strategies for digitized handwritten medieval documents," in Proc. Asian Information Retrieval Societies Conference, 2011, pp. 103-114.
20. M. Baechler, M. Liwicki, and R. Ingold, "Text line extraction using DMLP classifiers for historical manuscripts," in Proc. Int. Conf on Document Analysis and Recognition, 2013, pp. 1029-1033.
21. M. Baechler, J.-L. Blochle, and R. Ingold, "Semi-automatic annotation tool for medieval manuscripts," in Proc. Int. Conf. on Frontiers in Handwriting Recognition, 2010, pp. 182-187.
22. A. Fischer, M. Wüthrich, M. Liwicki, V. Frinken, H. Bunke, G. Viehhauser, and M. Stolz, "Automatic transcription of handwritten medieval documents," in Proc. Int. Conf. on Virtual Systems and Multimedia, 2009, pp. 137-142.
23. T. Ploetz and G. A. Fink, "Markov models for offline handwriting recognition: A survey," Int. Journal on Document Analysis and Recognition, vol. 12, no. 4, pp. 269-298, 2009.
24. A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber, "A novel connectionist system for improved unconstrained handwriting recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, pp. 855-868, 2009.
25. V. I. Levenshtein, "Binary codes capable of correcting deletions, insertions and reversals," Soviet Physics Doklady, vol. 10, no. 8, pp. 707-710, 1966.
26. K. Riesen and H. Bunke, "Approximate graph edit distance computation by means of bipartite graph matching," Image and Vision Computing, vol. 27, no. 7, pp. 950-959, 2009.
27. A. Fischer, C. Y. Suen, V. Frinken, K. Riesen, and H. Bunke, "A fast matching algorithm for graph-based handwriting recognition," in Proc. Int. Workshop on Graph-based Representations in Pattern Recognition, 2013, pp. 194-203.
28. A. Graves, "RNNLIB: A recurrent neural network library for sequence learning problems," http://sourceforge. net/projects/rnnl/.
29. A. Fischer, "Handwriting recognition in historical documents," Ph. D. dissertation, University of Bern, Switzerland, pages 96-100, 2012.