Stroke-model-based character extraction from gray-level document images

2001 ◽  
Vol 10 (8) ◽  
pp. 1152-1161 ◽  
Author(s):  
Xiangyun Ye ◽  
M. Cheriet ◽  
C.Y. Suen
Keyword(s):  
Gray Level ◽  
Document Images ◽  
Stroke Model ◽  
Model Based ◽  
Character Extraction

A hidden Markov model-based character extraction method

2008 ◽  
Vol 41 (9) ◽  
pp. 2890-2900 ◽  
Author(s):  
Songtao Huang ◽  
Majid Ahmadi ◽  
M.A. Sid-Ahmed
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Extraction Method ◽  
Hidden Markov ◽  
Model Based ◽  
Character Extraction

scholarly journals Design of an OCR System and its Hardware Implementation

2021 ◽  
Vol 9 (12) ◽  
pp. 159-174
Author(s):  
Gulfeshan Parween
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Character Recognition ◽  
Hardware Implementation ◽  
Gray Level ◽  
Document Images ◽  
English Alphabet ◽  
Barcode Recognition ◽  
Printed Text ◽  
Artificial Neural

Abstract: In this paper, we present a scheme to develop to complete OCR system for printed text English Alphabet of Uppercase of different font and of different sizes so that we can use this system in Banking, Corporate, Legal industry and so on. OCR system consists of different modules like preprocessing, segmentation, feature extraction and recognition. In preprocessing step it is expected to include image gray level conversion, binary conversion etc. After finding out the feature of the segmented characters artificial neural network and can be used for Character Recognition purpose. Efforts have been made to improve the performance of character recognition using artificial neural network techniques. The proposed OCR system is capable of accepting printed document images from a file and implemented using MATLAB R2014a version. Key words: OCR, Printed text, Barcode recognition

scholarly journals An Efficient Image Retrieval System using GLCM Features and Kullback-Leibler Divergence

2020 ◽  
Vol 9 (3) ◽  
pp. 3180-3184
Keyword(s):  
Retrieval System ◽  
Gaussian Mixture ◽  
Gray Level ◽  
Training Set ◽  
Relative Probability ◽  
Data Set ◽  
Model Based ◽  
Leibler Divergence ◽  
Image Retrieval System ◽  
Occurrence Matrix

Image processing is a process of extracting features from an image. The features of the image are extracted using the correlation model, based on Gray-Level Co-Occurrence Matrix (GLCM). Each of the images considered for data set are converted into gray level before applying Gaussian Mixture Model (GMM). The features extracted from GLCM are given as an input to the model-based technique so that the relative Probability Density Functions (PDF) are extracted. The comparison is carried out in the same manner by identifying the relative PDF of the training set and test data by using KullbackLeibler divergence method (KL-Divergence). In this paper an attempt is made for developing an effective model to retrieve the images based on features by considering GLCM and GMM. The results derived show that the proposed methodology is able to retrieve images more effectively.

A Doubly Degenerate Diffusion Model Based on the Gray Level Indicator for Multiplicative Noise Removal

2015 ◽  
Vol 24 (1) ◽  
pp. 249-260 ◽  
Author(s):  
Zhenyu Zhou ◽  
Zhichang Guo ◽  
Gang Dong ◽  
Jiebao Sun ◽  
Dazhi Zhang ◽  
...  
Keyword(s):  
Diffusion Model ◽  
Multiplicative Noise ◽  
Noise Removal ◽  
Degenerate Diffusion ◽  
Gray Level ◽  
Level Indicator ◽  
Model Based ◽  
Multiplicative Noise Removal
Sign in / Sign up

Export Citation Format

Share Document