کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4968989 1449847 2017 36 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Wavelet-based gender detection on off-line handwritten documents using probabilistic finite state automata
ترجمه فارسی عنوان
تشخیص جنسیت مبتنی بر موج بر روی اسناد دست خط خارج از خط با استفاده از ماشین حساب های حالت احتمالی محدود
کلمات کلیدی
تجزیه و تحلیل دست خط بدون خط، تشخیص جنسیت، تجزیه و تحلیل بافت، زیر باند موجک، دینامیک نمادین، اتوماتای ​​حالت حالت احتمالی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Detection of gender from handwriting of an individual presents an interesting research problem with applications in forensic document examination, writer identification and psychological studies. This paper presents an effective technique to predict the gender of an individual from off-line images of handwriting. The proposed technique relies on a global approach that considers writing images as textures. Each handwritten image is converted into a textur\ed image which is decomposed into a series of wavelet sub-bands at a number of levels. The wavelet sub-bands are then extended into data sequences. Each data sequence is quantized to produce a probabilistic finite state automata (PFSA) that generates feature vectors. These features are used to train two classifiers, artificial neural network and support vector machine to discriminate between male and female writings. The performance of the proposed system was evaluated on two databases, QUWI and MSHD, within a number of challenging experimental scenarios and realized classification rates of up to 80%. The experimental results show the superiority of the proposed technique over existing techniques in terms of classification rates.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Image and Vision Computing - Volume 59, March 2017, Pages 17-30
نویسندگان
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