American Journal of Electrical and Electronic Engineering
ISSN (Print): 2328-7365 ISSN (Online): 2328-7357 Website: Editor-in-chief: Naima kaabouch
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American Journal of Electrical and Electronic Engineering. 2015, 3(2), 51-63
DOI: 10.12691/ajeee-3-2-5
Open AccessArticle

Design Proposed Features Extraction Recognition System of Latin Handwritten Text Based on 3D-Discrete Multiwavelet Transform

Laith Ali Abdul-Rahaim1,

1Electrical Engineering Department, Babylon University, Babil, Iraq

Pub. Date: April 20, 2015

Cite this paper:
Laith Ali Abdul-Rahaim. Design Proposed Features Extraction Recognition System of Latin Handwritten Text Based on 3D-Discrete Multiwavelet Transform. American Journal of Electrical and Electronic Engineering. 2015; 3(2):51-63. doi: 10.12691/ajeee-3-2-5


On-line handwriting recognition is the task of determining what letters or words are present in handwritten text. It is of significant benefit to man-machine communication and can assist in the automatic processing of handwritten documents. It is a subtask of the Optical Character Recognition (OCR), whose domain can be machine-print only.The introduced system is a character-based recognition and it is a writer independent system. The recognition responsibility of the proposed system is for 52 character classes [uppercases (A-Z) and the lowercases (a-z)]. The suggested system includes the essential stages needed for most of the pattern recognition systems. These stages are the preprocessing stage, the features extraction stage, the pattern matching and classification stage and the postprocessing stage. The proposed method employs the 3 Dimensional Multiwavelet transform 3D-DMWTCS using multiresolution image decomposition techniques working together with multiple classification methods as a powerful classifier. The classification stage is designed by using a minimum distance classifier depending on Euclidean Distance which has a high speed performance. The system design also includes a modest postprocessing stage that makes a consistency between the recognized characters within the same word in relation to their upper and lower cases.The overall classification accuracy of proposed systems can be obtained are 95.305 percent with 3D-DMWTCS based on the Rimes database.


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