بازشناسی کلمات دست‌نویس فارسی به کمک تبدیل قطبی- لگاریتمی و مدل مخفی مارکوف
محورهای موضوعی : مهندسی برق و کامپیوترغلامرضا نادعلی‌نیا چاری 1 * , خشایار یغمایی 2 , حامد فضل‌اللهی آقاملک 3 , سيدمحمد رضوي 4
1 - دانشگاه سمنان
2 - دانشگاه سمنان
3 - دانشگاه بیرجند
4 - دانشگاه بیرجند
کلید واژه: تبدیل قطبی- لگاریتمی مدل مخفی مارکوف بازشناسی کلمات دست‌نویس فارسی,
چکیده مقاله :
در این مقاله یک سیستم بازشناسی کلمات فارسی معرفی می‌شود که از خودهمبستگی محلی مرتبه بالای تصویر قطبی- لگاریتمی برای استخراج ویژگی از زیرکلمات فارسی استفاده می‌کند. این شیوه استخراج ویژگی باعث می‌شود سیستم در مقابل تغییرات نگارشی مانند تغییر مقیاس‌های خطی و چرخش مقاوم شود. همچنین به کمک تبدیل قطبی- لگاریتمی، نمونه‌برداری از تصویر زیرکلمه به صورتی انجام شده که بیشترین نمونه‌ها در یک ناحیه خاص متمرکز باشد. در روش ارائه‌شده از مدل مخفی مارکوف گسسته به عنوان طبقه‌بند و همچنین برای افزایش امنیت و دقت خروجی سیستم بازشناسی از یک شبکه واژه‌نامه استفاده شده و برای ارزیابی سیستم از پایگاه داده ایران‌شهر استفاده شده بود. مقایسه نتایج حاصل از روش پیشنهادی با نتایج سایر روش‌های استخراج ویژگی مؤید این است که سیستم بازشناسی پیشنهاد‌شده در این مقاله از حساسیت کمتری نسبت به تغییرات نگارشی برخوردار است.
In this paper a recognition system for Persian words is introduced which utilizes the local higher order of the log-polar image autocorrelation for feature extraction of Persian sub-words. This feature extraction technique brings up leads to a system robustness in cases of writing variations alteration like scaled or rotated handwritings. Also using the log-polar transform, the sub-word image sampling will be performed so that most of acquired samples will be centered in a certain area. The proposed method uses the discrete Hidden Markov’s Model (HMM) as a classifier. Furthermore a net of dictionaries were employed to increase the reliability and precision of the system output. Finally, the Iran-Shahr database is utilized to evaluate the system performance. Comparing the results of the proposed method and other previous methods, proves that a less sensitivity has been achieved by the proposed method about handwriting variations.
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