@article{MAKHILLIJSSCEA201912328787,
    title = {Persian Handwritten Digit Recognition using Support Vector Machine},
    journal = {International Journal of System Signal Control and Engineering Application},
    volume = {12},
    number = {3},
    pages = {35-39},
    year = {2019},
    issn = {1997-5422},
    doi = {ijssceapp.2019.35.39},
    url = {https://makhillpublications.co/view-article.php?issn=1997-5422&doi=ijssceapp.2019.35.39},
    author = {Mojtaba,Abbas and},
    keywords = {Histogram of oriented gradients,principle component analysis,support vector machine,cheques,samples},
    abstract = {Handwritten digit recognition has a special
importance and also different applications such as
detecting handwritten addresses, cheques and documents.
Persian handwritten digit classification is facing more
difficulties due to different handwritten types and also
inter-class similarities and intra-class differences. In this
study, a method for Persian handwritten digits detection
is proposing. In the proposed method, a mixture of
Histogram of Oriented Gradients (HOG), 4-side profiles
of the digit image and some horizontal and vertical
samples of it is used. Then, the dimension of the feature
vector is reduced by using Principle Component Analysis
(PCA). In the classification step, Support Vector Machine
(SVM) is used. The proposed method is applied on the
HODA database. The results shows 99.25% in detection
accuracy which is an adequate rate due to existing
unacceptable samples in the database as well as achieving
great improvement comparing to other existing methods.}
    }