ادغام شبکه‌های عصبی بر اساس یادگیری با همبستگی منفی در بازشناسی برون‌خط کلمات دست‌نویس
الموضوعات :
سیدعلی‌اصغر عباس‌زاده آرانی
1
(دانشگاه تربیت مدرس)
احسان‌اله کبیر
2
(دانشگاه تربیت مدرس)
الکلمات المفتاحية: ادغام طبقه‌بندها بازشناسی کلمات دست‌نویس پرسپترون چندلایه یادگیری با همبستگی منفی,
ملخص المقالة :
در این تحقیق، یک روش طبقه‌بندی جمعی بر اساس یادگیری با همبستگی منفی برای بازشناسی کل‌نگر کلمات دست‌نویس با حجم محدود پیشنهاد می‌شود. در این روش مجموعه داده آموزشی پس از پیش‌پردازش و استخراج ویژگی به طبقه‌بندهای پایه پرسپترون چندلایه اعمال می‌شود. سپس شبکه‌های عصبی پایه به روش یادگیری با همبستگی منفی، آموزش داده شده و از این طریق گوناگون می‌شوند. هنگامی که داده‌های آزمایشی پس از استخراج ویژگی به طبقه‌بندهای پایه اعمال می‌شوند، هر طبقه‌بند پایه خروجی نسبتاً متفاوتی را تولید می‌کند. با ادغام خروجی طبقه‌بندهای پایه، خروجی نهایی سیستم به دست می‌آید. برای آزمایش روش پیشنهادی از سه نوع ویژگی شامل ویژگی‌های مبتنی بر منطقه‌بندی، گرادیان تصویر و کد زنجیره‌ای کانتور استفاده شده است. در آزمایش‌هایی که روی 775 تصویر از نام 31 مرکز استان کشور، از مجموعه داده "ایرانشهر" انجام شده است، استفاده از ویژگی‌های مبتنی بر گرادیان و آموزش 6 شبکه پرسپترون با همبستگی منفی و ادغام آنها از طریق رأی‌گیری، میانگین نرخ بازشناسی برابر با 10/96 درصد را به دست داده است. سپس خطاهای روش پیشنهادی تحلیل و ردیابی شده است.
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