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Manuscript Character Recognition Overview of features for the Feature Vector
Abstract
The image recognizing process requires the identification of every logical object that compose every image, which first implies to recognize it as an object (segmentation) and then identify which object is, or at least which is the most likely one from the universe of objects that can be recognized (recognition). During the segmentation process, the aim is to identify as many objects that compose the images as possible. This process must be adapted to the universe of all objects that are looked for, which can vary from printed or manuscript characters to fruits or animals, or even fingerprints. Once all objects have been obtained, the system must carry on to the next step, which is the identification of the objects based on the called universe. In other words, if the system is looking for fruits, it must identify univocally fruits from apples and oranges; if they are characters, it m ust iden tify th e character “a” from the rest of the alphabet, and so on. In this document, the character recognition step has been studied. More specifically, which methods to obtain characteristics exist (advantages and disadvantages, implementations, costs). There is also an overview about the feature vector, in which all features are stored and analyzed in order to perform the character recognition itself.
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References
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[2] Marisa R. De Giusti, Maria Marta Vila, Gonzalo Luján V illarreal. “M an uscript D ocument Digitalization and recognition : a first approach”. Journal of Computer Science and Technology. Vol 5. No. 3. 158-163,2005.
[3] William Prat, John Wiley & Sons, Digital Image Processing, 1991, Second Edition
[4] Gonzalez Rafael, Woods, Addison-Wesley Digital Image Processing, 1992, Second Edition
[5] Michel Weinfeld, “R econ naissan ce d'ecriture manuscrite: segmentation de m ots”. Départem ent d'Enseignement et de Recherche en Informatique. École Polytechnique, Paris, France
[6] L . Fletch er and R . K asturi. “A robust algorithm for text string separation from mixed text/graphics images”. IEEE Transactions on Pattern Analysis and Machina Intelligence, 10:910-918. 1988
[7] R . Manmatha and Nitin Srimal. “Scale Space Technique for Word Segmentation in Handwritten Documents”. Computer Science Departament, University of Massachusetts
[8] R. Manmatha, Chengfeng Han, E. M. Riseman and W . B . roft. “In dexin g H an dw rittin g using Word Matching”. Center for In form ation Retrieval, Computer Science Departament, University of Massachusetts
[9] Nicolas Nonmarché. “A lgorithm es de fourm is artificielles: applications à la classification et à l'optimisation ”, 2000. Université F rancois R abelais Tours.
[10] R. Manmatha, Chengfeng Han and E. M. Riseman. “Word Spotting: A N ew Aproach to Indexing Handwriting”. Center for Intelligent Information Retrieval, Computer Science Departament, University of Massachusetts.
[11] H. Asada y M. Brady. The curvature primal sketch. IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(1):2-14, 1986
[12] M.H. Han, D. Jang, y J. Foster. Identification of corners points of two-dimensional images using a line search method. Pattern Recognition, 22(1):13--20, 1989.
[13] G. Medioni y Y. Yasumoto. Corner detection and curve representation using cubic B-splines. Computer Vision, Graphics, and Image Processing, 39:267--278, 1987.
[14] B. Bell y L.F. Pau. Contour tracking and corner detection in a logic programming environment. IEEE Trans. on Pattern Analysis and Machine Intelligence, 12(9):913--917, September 1990.
[15] R. Deriche y O.D. Faugeras. 2d curve matching using high curvature points: Application to stereo vision. In Proc. of the 10th. International Conference of Pattern Recognition, pages 240--242, 1990.
[16] Alejandro H. Toselli. Tesis doctoral: Reconocimiento de texto manuscrito continuo.. Universidad de Valencia. 2004
[17] Jianying Hu, Michael K. Brown, William Turin. HMM Based OnLine Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 10 (18):1039-1045. Oct. 1996
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Published
2006-10-02
How to Cite
De Giusti, M. R., Vila, M. M., & Villareal, G. L. (2006). Manuscript Character Recognition Overview of features for the Feature Vector. Journal of Computer Science and Technology, 6(02), 92–98. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/821
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Original Articles
