Digits and Character Recognition using KNN
نویسندگان
چکیده
منابع مشابه
Optical Character Recognition: Classification of Handwritten Digits and Computer Fonts
Optical character Recognition (OCR) is an important application of machine learning where an algorithm is trained on a data set of known letters/digits and can learn to accurately classify letters/digits. A variety of algorithms have shown excellent accuracy for the problem of handwritten digits, 4 of which are looked at here. Additionally, we attempt to extend these techniques to the harder pr...
متن کاملPrinted and Handwritten Character &Number Recognition of Devanagari Script using SVM and KNN
Recognition of Devanagari scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character. In this paper we have compared SVM and KNN on handwritten as well as on printed character and numerical databa...
متن کاملThree supervised learning methods on pen digits character recognition dataset
Supervised learning is a broad field that encompasses a number of methods, which can generally be classificed into two categories: parametric and nonparametric. In the parametric methods, it is assumed that the forms of the underlying density functions are known. The problem of estimating unknown functions can be reduced to estimating some values of parameters. In contrast, in the nonparametric...
متن کاملFace Recognition Using Bagging Knn
In this paper a novel ensemble based techniques for face recognition is presented. In ensemble learning a group of methods are employed and their results are combined to form the final results of the system. Gaining the higher accuracy rate is the main advantage of this system. Two of the most successful wrapping classification methods are bagging and boosting. In this paper we used the K neare...
متن کاملIsolated Printed Arabic Character Recognition Using KNN and Random Forest Tree Classifiers
Classification step is one of the most important tasks in any recognition system. This step depends greatly on the quality and efficiency of the extracted features, which in turn determines the efficient and appropriate classifier for each system. This study is an investigation of using both KNearest Neighbor (KNN) and Random Forest Tree (RFT) classifiers with previously tested statistical feat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal for Research in Applied Science and Engineering Technology
سال: 2019
ISSN: 2321-9653
DOI: 10.22214/ijraset.2019.12126