نقدم في بحثنا طريقة بسيطة, لتمييز صور المحارف المكتوبة يدوياً بالاعتماد
على عمليات التنقيب التنبئي. و ذلؾ من خلال استخراج إحداثيات النقاط السوداء من
صور المحارف الثنائية اللون (أسود, أبيض) المستخدمة في مراحل التدريب و الاختبار.
و تخزينها في قاعدة بيانات, و فق بنية مناسبة لعمليات التنقيب التنبئي (بيانات تدريب
و بيانات اختبار). و من ثم استخدام بيانات التدريب المستخرجة لبناء نموذج تنبئي يساعد
على تمييز صور الاختبار, اعتماداً على خصائصها المستخرجة. و قد أجرينا عدة
اختبارات على عينات مختلفة من صور المحارف المكتوبة يدوياً, و حصلنا على نتائج
دقيقة, ضمن الشروط المطلوبة.
In this research, we offered a new and simple way of
Handwriting Characters Recognition. This way extracts positions of
the black points from binary images (black, white) according to
certain coordinates which are used in the stages of training and
testing. The extracted positions are stored in a database according
to appropriate structure for predictive data mining.
We used training data to build a predictive model which helps
in Recognition testing data depending on the data stored in the
database. We have conducted a number of tests on different
samples of handwriting character images. We got accurate results,
within the required conditions.
References used
AGGARWAL, CH ,2014–Data Classification Algorithms and Applications. First Edition, Taylor & Francis Group, LLC, New York, USA,64P
ALPAYDIN, E, 2010-Introduction to Machine Learning. Second Edition, Cambridge, Massachusetts London, England, 579p
BARBER,D,2010-Bayesian Reasoning and Machine Learning. First Edition, Cambridge University Press, London, England, 610p
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