This research applied the Euclidean distance technique to generate a system of Thai handwritten
character recognition. The system consists of four main components which include: 1) Image Acquisition, 2)
Image Pre-processing, 3) Recognition, and 4) Display Result. All training and testing handwritten characters
in this research used all Thai native people to write them for avoiding invalid shape of Thai character. The
character images fed to the training part totaling 3,513 characters. Out of 878 Thai handwritten characters
tested, it was found that the system could recognize (accept) 716 characters or 81.55%, while rejecting 61
characters or 6.95% and misrecognizing 101 characters or 11.50%. We tested the system with 50 Japanese
handwritten characters and 25 invalid Thai handwritten character shape, it was found that the system could
reject 47 characters or 62.67% while misrecognizing 28 characters or 37.33%.
The idea of this project development is to improve the concept of human face recognition
that has been studied in order to apply it for a more precise and effective recognition of human faces,
and offered an alternative to agencies with respect to their access-departure control system. To
accomplish this, a technique of calculation of distances between face features, including efficient
face recognition though a neural network, is used. The system uses a technique of image processing
consisting of 3 major processes: 1) preprocessing or preparation of images, 2) feature extraction
from images of eyes, ears, nose and mouth, used for a calculation of Euclidean distances between
each organ; and 3) face recognition using a neural network method. Based on the experimental
results from reading image of a total of 200 images from 100 human faces, the system can correctly
recognize 96 % with average access time of 3.304 sec per image.
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