بازشناسی ارقام دست‍نویس فارسی مبتنی بر ترکیب ماشین‍های بردار پشتیبان به روش فازی نوع دو بازه ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1- گروه مهندسی برق- واحد شیراز، دانشگاه آزاد اسلامی- شیراز- ایران

چکیده

مساله بازشناسی خودکار محتوای دستنوشتهها، همواره مورد توجه بسیاری از محققان بوده است. در این مقاله، یک سیستم ترکیبی برای افزایش دقت تشخیص ارقام دستنویس فارسی ارائه شده است. روش پیشنهادی شامل یک فرایند آمادهسازی و دو مرحله اصلی است. در فرایند آماده سازی، چندین عملیات پیش پردازش بر روی تصاویر انجام ‌شده و پس از استخراج ویژگی‌ها، از الگوریتم بهینه‌سازی اجتماع ذرات چندهدفه برای انتخاب ویژگیهای مؤثر استفاده‌ شده است. آنگاه متناظر هر تصویر، این ویژگیهای بهینه به عنوان داده ورودی به طبقهبندها داده میشود. در مرحله اصلی اول، به کمک مجموعه دادههای یادگیری، سه ماشین بردار پشتیبان مختلف ساخته میشود. برای دستیابی به نتایج بهتر، الگوریتم جستجوی گرانشی بهترین جِرم تطبیقی، برای تنظیم پارامترهای این ماشینها به کار گرفته شده است. در مرحله اصلی دوم، یک سیستم استنتاج فازی نوع دو بازهای، خروجیهای سه ماشین بردار پشتیبان را دریافت میکند و با ترکیب آنها، تخمین دقیقتری از عدد موجود در تصویر ارائه میدهد. نتایج اِعمال روش پیشنهادی به مساله بازشناسی ارقام دستنویس فارسی اسکن شده در پایگاه داده استاندارد HODA نشان داده است که این الگوریتم در مقایسه با سایر روش‌های موجود، دارای مقادیر بالای دقت، صحت و فراخوان می‌باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Farsi Handwritten Digits Recognition based on Interval Type-II Fuzzy Fusion of Support Vector Machines

نویسندگان [English]

  • Azar Mahmoodzadeh
  • hamed agahi
Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
چکیده [English]

The problem of automatic handwritten context recognition has received considerable attention of many researchers. In this paper, a fusion system is proposed to enhance the recognition accuracy of Farsi handwritten digits. The proposed approach consists of a prepration process and two main phases. In the prepration process, some pre-processing operations are performed on the image. Then some features are extracted, among which a multi-objective particle swarm optimization selects more effective ones. For every image, these optimal features are given as the input data to the classifiers. In the first main phase, training datasets are used to construct three different SVMs. In order to achieve better results, the adaptive best-mass gravitational search algorithm is utilized to adjust the SVMs parameters. In the second main phase, an interval type–II fuzzy inference system receives the SVMs outputs and by combining them, it presents a more accurate estimation of the digit in the image. The results of applying the proposed approach to the problem of scanned Farsi handwritten digits in the standard HODA database demonstrated that this algorithm attains high accuracy, precision and recall performance indices, comparing to other existing methods.

کلیدواژه‌ها [English]

  • Classification
  • classifiers ensemble
  • Farsi handwritten digits
  • feature selection
  • interval type–II fuzzy inference system
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