Image analysis – Learning systems – Trainable classifiers or pattern recognizers
Reexamination Certificate
2011-08-23
2011-08-23
Ahmed, Samir A (Department: 2624)
Image analysis
Learning systems
Trainable classifiers or pattern recognizers
C382S186000
Reexamination Certificate
active
08005294
ABSTRACT:
A cursive character handwriting recognition system includes image processing means for processing an image of a handwritten word of one or more characters and classification means for determining an optimal string of one or more characters as composing the imaged word. The processing means segments the characters such that each character is made up of one or more segments and determines a sequence of the segments using an over-segmentation-relabeling algorithm. The system also includes feature extraction means for deriving a feature vector to represent feature information of one segment or a combination of several consecutive segments. The over-segmentation-relabeling algorithm places certain segments considered as diacritics or small segments so as to immediately precede or follow a segment of the associated main character body. Additionally, the system also includes classification means that processes each string of segments and outputs a number of optimal strings which could be matched against a given lexicon.
REFERENCES:
Lorigo et al : “Segmentation and pre-recognition of Arabic handwriting”, Proc. ICDAR'5, IEEE, 2005.
M. -Y. Chen, A. Kundu and J. Zhou, “Off-line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network,” IEEE Trans. on PAMI, vol. 16, 5, 481-496, May 1994.
M. -Y. Chen, A. Kundu and S. N. Srihari, “Variable Duration HMM and Morphological Segmentation for Handwritten Word Recognition,” IEEE Trans. on Image Proc., vol. 4, 12, 1675-1688, Dec. 1995.
A. Kundu, Y. He and M. -Y. Chen, “Alternatives to Variable Duration HMM in Handwritten Word Recognition,” IEEE Trans. on PAMI, vol. 20, 11, 1275-1280, 1998.
A. Kundu, P. Bahl and Y. He, “Recognition of Handwritten word: First and Second Order Hidden Markov Model Based approach,” Pattern Recognition, vol. 22, No. 3, 283-297, 1989.
Almuallim, H. and Yamaguchi, S., “A Method of Recognition of Arabic Cursive Handwriting,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 9, 1987.
Khorsheed, M.S. and W.F. Clocksin, “Off-line Arabic word recognition using a Hidden Markov Model,” In Statistical Methods for Image Processing—A Satellite Conference of the 52-nd ISI Session, Uppsala., 1999.
Kanoun, S., Ennaji, A., Lecourtier, Y. and Alimi, A. M. “Linguistic Integration Information in the AABTAS Arabic Text Analysis System,” Proc. of International Workshop on Frontiers in Handwriting Recognition (IWFHR), vol. 8, pp. 389-394, Aug. 2002.
Abuhaiba, Holt, M. and Datta, S., “Processing of Binary Images of Handwritten Cursive Arabic Characters,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 16, No. 6, Jun. 1994.
“Seventh International Conference Document Analysis and Recognition”, Aug. 3 to 6, 2003.
ICDAR 2005 Arabic Handwriting Recognition Competition, V. Märgner, M. Pechwitz, H. El Abed.
A. Kundu and L. Van Guilder, “Quick International Character Recognition (QUICK)”, Powerpoint Presentation, Mitre Technology Symposium, Apr. 2005.
L. Van Guilder, “Improved Foreign Language OCR”, Powerpoint Presentation, Mitre Technology Symposium, Apr. 2004.
Rabiner, L.R., “A tutorial on hidden Markov model and selected applications in speech processing,” Proceedings of IEEE 77(2):257-289 (1989).
Hines Tom
Huyck Ben
Kundu Amlan
Phillips Jon
Van Guilder Linda C.
Ahmed Samir A
Li Ruiping
Sterne Kessler Goldstein & Fox P.L.L.C.
The Mitre Corporation
LandOfFree
Cursive character handwriting recognition system and method does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Cursive character handwriting recognition system and method, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Cursive character handwriting recognition system and method will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2790076