عنوان المقالة: A NEW MODEL OF ARABIC HANDWRITTEN ECOGNITION USING COMBINATION BETWEEN DWT WITH DATA REDUCTION METHOD
الاستاذ المساعد الدكتورة اسماء شاكر عاشور | Assist. Prof. Dr. Asmaa Shaker Ashoor | 18402
نوع النشر
مجلة علمية
المؤلفون بالعربي
المؤلفون بالإنجليزي
ANWAR YAHYA EBRAHIM, ASMAA SHAKER ASHOOR
الملخص الانجليزي
The objective of this research was to improve an automatic writer recognition technique for off-line Arabic handwritten text. This objective was met by developing a new method which not only addressed the problems in existing techniques but also realized better outcomes than the current solutions on this issue. The projected method is based on Discrete Wavelet Transform (DWT) to extract feature. Also important features for Arabic handwritten alphanumeric character recognition to aid the verification step. This scheme involves of four phases: preprocessing, feature extraction, important features and finally writing recognition. Important features are constructed by data reduction method. After collecting all important features of each character. To recognize fonts, which improves the classification performance. Then decision tree classifier was used for writing recognition. In this paper provide good accuracy dealing with important attributes. The suggested ideal is fast and reliable. The proposed technique has been achieved very promising results, with a validation accuracy of 98.45% for HACDB database.
تاريخ النشر
15/10/2018
رقم المجلد
96
رقم العدد
19
ISSN/ISBN
1817-3195
رابط DOI
www.jatit.org
رابط الملف
تحميل (485 مرات التحميل)
الكلمات المفتاحية
Discrete Wavelet Transform (DWT), Data Reduction Method, (DT) Decision Tree Classifier, Writing Recognition
رجوع