Script Recognition using GLCM and DWT Features
نویسندگان
چکیده
منابع مشابه
Improving Texture Recognition using Combined GLCM and Wavelet Features
Texture is an important perceptual property of images based on which image content can be characterized and searched for in a Content Based Search and Retrieval (CBSR) system. This paper investigates techniques for improving texture recognition accuracy by using a set of Wavelet Decomposition Matrices (WDM) in conjunction with Grey Level Co-occurrence Matrices (GLCM). The texture image is decom...
متن کاملDWT Based Fingerprint Recognition using Non Minutiae Features
Forensic applications like criminal investigations, terrorist identification and National security issues require a strong fingerprint data base and efficient identification system. In this paper we propose DWT based Fingerprint Recognition using Non Minutiae (DWTFR) algorithm. Fingerprint image is decomposed into multi resolution sub bands of LL, LH, HL and HH by applying 3 level DWT. The Domi...
متن کاملVehicle Logo Recognition Using Image Matching and Textural Features
In recent years, automatic recognition of vehicle logos has become one of the important issues in modern cities. This is due to the unlimited increase of cars and transportation systems that make it impossible to be fully managed and monitored by human. In this research, an automatic real-time logo recognition system for moving cars is introduced based on histogram manipulation. In the proposed...
متن کاملCopy Move Forgery Detection Using Glcm Based Statistical Features
The features Gray Level Co-occurrence Matrix (GLCM) are mostly explored in Face Recognition and CBIR. GLCM technique is explored here for Copy-Move Forgery Detection. GLCMs are extracted from all the images in the database and statistics such as contrast, correlation, homogeneity and energy are derived. These statistics form the feature vector. Support Vector Machine (SVM) is trained on all the...
متن کاملDWT features performance analysis for automatic speech recognition of Urdu
This paper presents the work on Automatic Speech Recognition of Urdu language, using a comparative analysis for Discrete Wavelets Transform (DWT) based features and Mel Frequency Cepstral Coefficients (MFCC). These features have been extracted for one hundred isolated words of Urdu, each word uttered by ten different speakers. The words have been selected from the most frequently used words of ...
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: IJARCCE
سال: 2015
ISSN: 2278-1021
DOI: 10.17148/ijarcce.2015.4157