ادغام شبکه‌های عصبی بر اساس یادگیری با همبستگی منفی در بازشناسی برون‌خط کلمات دست‌نویس
محورهای موضوعی : مهندسی برق و کامپیوتر
سیدعلی‌اصغر عباس‌زاده آرانی
1
(دانشگاه تربیت مدرس)
احسان‌اله کبیر
2
(دانشگاه تربیت مدرس)
کلید واژه: ادغام طبقه‌بندها بازشناسی کلمات دست‌نویس پرسپترون چندلایه یادگیری با همبستگی منفی,
چکیده مقاله :
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.
In this study, an ensemble classification method, based on negative correlation learning, is used for holistic recognition of handwritten words with limited vocabulary. In this method, training data set, after preprocessing and feature extraction, is applied to the base Multilayer Perceptron classifiers. These classifiers are trained by negative correlation learning to make them diverse. Features extracted from a test input are applied to the base classifiers, which produce somehow diverse outputs. By combining these outputs, the final output of the system is obtained. For experiments, three feature sets based on zoning, gradient image and contour chain code are extracted from the images. In experiments, performed on 775 images of 31 Province centers from "Iranshahr" dataset, when gradient-based features were used to train 6 Multilayer Perceptron classifiers by negative correlation, by Fusion the outputs of these classifiers through voting, an average recognition rate of 96.10 percent is achieved.
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