شماره ركورد كنفرانس :
3297
عنوان مقاله :
Recognizing Persian Handwritten Words Using Deep Convolutional Networks
عنوان به زبان ديگر :
Recognizing Persian Handwritten Words Using Deep Convolutional Networks
پديدآورندگان :
Sabzi Rasool Department of Electrical and Computer Engineering - University of Hormozgan - Bandar Abbas - Iran , Fotoohinya Zahra Department of Electrical and Computer Engineering - University of Hormozgan - Bandar Abbas - Iran , Salkhorde Zeinab Department of Electrical and Computer Engineering - University of Hormozgan - Bandar Abbas - Iran , Khalili Abdullah Department of Electrical and Computer Engineering - University of Hormozgan - Bandar Abbas - Iran , Golzari Shahram Department of Electrical and Computer Engineering - University of Hormozgan - Bandar Abbas - Iran , Behravesh Sajjad Department of Computer Eng. & IT - Shiraz University of Technology - Shiraz - Iran , Akbarpour Shahin Department of Computer and Mathematics - Islamic Azad University of Shabestar - Iran
كليدواژه :
Batch normalization , Persian handwritten word , ( Convolutional Neural Network (CNN , Deep learning
عنوان كنفرانس :
نوزدهمين سمپوزيوم بين المللي هوش مصنوعي و پردازش سيگنال
چكيده لاتين :
handwritten word recognition is an active research area
due to numerous commercial applications in offline and online
recognition systems. The diversity and complexity of Persian
handwritten words makes them more difficult to recognize. In
current methods, discriminative features are manually extracted
from images by humans so their performance depends on human
creativity. This process is called shallow learning. In this study,
deep Convolutional Neural Networks (CNNs), a widely used type
of deep learning, is employed to automatically extract the
discriminative features. Deep learning is able to discover complex
structure (discriminative feature here) in large datasets. First in
the proposed method, a preprocessing algorithm converts the
images to equal size while maintaining handwritten words
structure. Then, the images are given to two different
architectures of CNNs, AlexNet and GoogLeNet with and without
batch normalization. Finally, the proposed method is evaluated on
“IRANSHAHR” dataset which includes 15383 images of 503
different city names of Iran. Experimental results show that
GoogLeNet with preprocessed data and batch normalization
achieves higher accuracy (99.13%) and outperforms the current
methods.
