Finding words in alphabet soup: Inference on freeform character recognition for historical scripts

Pattern Recognition

Volumn 42, Issue 12, 2009, Pages 3338-3347

  Howe, Nicholas R ^a   Feng, Shaolei ^b   Manmatha, R ^b  

^a SMITH COLLEGE   (United States)

^b Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

Character recognition; Cursive text; Historical text

Indexed keywords

CURSIVE TEXT; DETECTION METHODS; FREEFORMS; HANDWRITTEN TEXTS; HISTORICAL TEXT; OUT-OF-VOCABULARY WORDS; PHYSICAL SEPARATION; PRINTED DOCUMENTS; WORD RECOGNITION;

CHARACTER RECOGNITION; FEEDFORWARD NEURAL NETWORKS; MODELS;

HIDDEN MARKOV MODELS;

EID: 68249106566     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.01.012     Document Type: Article

References (32)

1. Vinciarelli A., Bengio S., and Bunke H. Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Trans. Pattern Anal. Mach. Intell. 26 6 (2004) 709-720
2. T.M. Rath, R. Manmatha, V. Lavrenko, A search engine for historical manuscript images, in: 27th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004, pp. 369-376.
3. N. Howe, T. Rath, R. Manmatha, Boosted decision trees for word recognition in handwritten document retrieval, in: 28th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005, pp. 377-383.
4. Plamondon R., and Srihari S.N. On-line and off-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22 1 (2000) 63-84
5. Vinciarelli A. A survey on off-line cursive script recognition. Pattern Recognition 35 7 (2002) 1433-1446
6. Madhvanath S., and Govindaraju V. The role of holistic paradigms in handwritten word recognition. IEEE Trans. Pattern Anal. Mach. Intell. 23 2 (2001) 149-164
7. Adamek T., O'Connor N.E., and Smeaton A.F. Word matching using single closed contours for indexing handwritten historical documents. Int. J. Doc. Anal. Recogn. 9 (2007) 153-165
8. V. Lavrenko, T. Rath, R. Manmatha, Holistic word recognition for handwritten historical documents, in: Proceedings of the IEEE Workshop on Document Image Analysis for Libraries, 2004, pp. 278-287.
9. Edwards J., Teh Y.W., Forsyth D., Bock R., Maire M., and Vesom G. Making Latin manuscripts searchable using gHMM's. Adv. Neural Inform. Process. Syst. 17 (2005) 385-392
10. Edwards J., and Forsyth D. Searching for character models. Adv. Neural Inform. Process. Syst. 18 (2006) 331-338
11. M. Decerbo, P. Natarajan, R. Prasad, E. MacRostie, A. Ravindran, Performance improvements to the bbn byblos OCR system, in: Proceedings of the 8th International Conference on Document Analysis and Recognition, 2005, pp. 411-415.
12. Torralba A., Murphy K.P., and Freeman W.T. Sharing visual features for multiclass and multiview object detection. IEEE Trans. Pattern Anal. Mach. Intell. 29 5 (2007) 854-869
13. Marti U.-V., and Bunke H. Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. Int. J. Pattern Recognit. Artif. Intell. 15 1 (2001) 65-90
14. T. Crackel, Private communication.
15. Rath T., and Manmatha R. Word spotting for historical documents. Int. J. Doc. Anal. Recogn. 9 2-4 (2007) 139-152
16. S. Feng, R. Manmatha, Exploring the use of conditional random field models and HMMs for historical handwritten document recognition, in: Proceedings of the 2nd IEEE International Conference on Document Image Analysis for Libraries, 2006, pp. 30-37.
17. Mori G., and Malik J. Recognizing objects in adversarial clutter: breaking a visual captcha. Comput. Vis. Pattern Recogn. 1 (2003) 134-141
18. Bar-Yosef I., Beckman I., and Kedem K. Binarization, character extraction, and writer identification of historical Hebrew calligraphy documents. Int. J. Doc. Anal. Recogn. 9 (2007) 89-99
19. Ntzios K., Gatos B., Pratikakis I., and Konidaris T. An old greek handwritten OCR system based on an efficient segmentation-free approach. Int. J. Doc. Anal. Recogn. 9 (2007) 179-192
20. S. Impedovo, Hidden Markov models in handwriting recognition, in: S. Impedovo (Ed.), Fundamentals in Handwriting Recognition, 1994, pp. 7-39.
21. S. Feng, R. Manmatha, A hierarchical, HMM-based automatic evaluation of OCR accuracy for a digital library of books, in: ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'06), 2006, pp. 109-118.
22. J. He, D.M.Q. Do, A.C. Downton, A comparison of binarization methods for historical archive documents, in: 8th International Conference on Document Analysis and Recognition, Seoul, Korea, 2005, pp. 538-542.
23. Learned-Miller E. Data-driven image models through continuous joint alignment. IEEE Trans. Pattern Anal. Mach. Intell. 28 2 (2006) 236-250
24. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, Comput. Vis. Pattern Recogn. (2005) 886-893.
25. A. Vedaldi, P. Favaro, E. Grisan, Boosting invariance and efficiency in supervised learning, in: International Conference on Computer Vision, 2007, pp. 1-8.
26. L. Wolf, I. Martin, Regularization through feature knock out, Technical Report AIM-2004-025, MIT, Cambridge, 2004.
27. Bishop C. Pattern Recognition and Machine Learning (2006), Springer, Berlin
28. Mease D., Wyner A.J., and Buja A. Boosted classification trees and class probability/quantile estimation. J. Mach. Learn. Res. 8 (2007) 409-439
29. Jelinek F. Statistical Methods for Speech Recognition (1997), MIT Press, Cambridge, MA
30. S. Feng, Statistical models for text query-based image retrieval, Ph.D. Thesis, Uinversity of Massachusetts, Amherst, May 2008.
31. J.T. Favata, G. Srikantan, S.N. Srihari, Handprinted character/digit recognition using a multiple feature/resolution philosophy, in: International Workshop on Frontiers in Handwriting Recognition, 1994, pp. 57-66.
32. J.A. Rodriguez, F. Perronin, Local gradient features for word spotting in unconstrained handwritten documents, in: International Conference on Frontiers in Handwriting Recognition, 2008.