Natural Image Character Recognition Using Oriented Basic Image Features

2011 ◽  
Author(s):  
Andrew J. Newell ◽  
Lewis D. Griffin
Keyword(s):  
Character Recognition ◽  
Image Features ◽  
Natural Image ◽  
Basic Image

scholarly journals An Effective feature set in Quality Estimation of Natural Scene Statistics Images

2019 ◽  
Vol 9 (2) ◽  
pp. 2881-2888
Keyword(s):  
Image Quality Assessment ◽  
Image Features ◽  
Quality Score ◽  
Natural Image ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Quality Estimation ◽  
Objective Quality ◽  
Quality Value

Quality estimation in images is an area which demands high attention of researchers. Many recent algorithms in Image quality assessment relies on the computation of definite values from the image or comparison with the original pristine image. Here, we propose the extraction of a set of specific features from image and processing is done on these extracted features to obtain the objective quality score. The detailed inspection of behaviour of this set of highly specific image features extracted through less complex mathematical procedure from a collection good quality and low quality set of Natural Scene Statistics images available in LIVE dataset is elaborated in this work. Our studies and results are compared with the subjective opinion value and is proven to be accurate. The obtained results are demonstrated using statistical and graphical manner for promptness in understanding the nature of quality of the image. Thus the proposed feature set is proven to be complete in assessing the quantitative quality value of any Natural image.

scholarly journals Computer Graphic and Photographic Image Classification using Local Image Descriptors

2017 ◽  
Vol 67 (6) ◽  
pp. 654 ◽  
Author(s):  
Gajanan K Birajdar ◽  
Vijay H Mankar
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Computer Graphic ◽  
Image Features ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Natural Image ◽  
Discrete Wavelet ◽  
Feature Subset ◽  
Graphic Images

<p class="p1">With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this article, a method is proposed based on discrete wavelet transform based binary statistical image features to distinguish computer graphic from photo graphic images using the support vector machine classifier. Textural descriptors extracted using binary statistical image features are different for computer graphic and photo graphic which are based on learning of natural image statistic filters. Input RGB image is first converted into grayscale and decomposed into sub-bands using Haar discrete wavelet transform and then binary statistical image features are extracted. Fuzzy entropy based feature subset selection is employed to choose relevant features. Experimental results using Columbia database show that the method achieves good detection accuracy.</p>

Accuracy analysis of Japanese machine translation based on machine learning and image feature retrieval

2020 ◽  
pp. 1-12
Author(s):  
Gang Song
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Character Recognition ◽  
Image Data ◽  
Image Features ◽  
Image Feature ◽  
Learning Technology ◽  
Japanese Character ◽  
Knowledge Support ◽  
Translation Methods

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research.

Image Compression Concepts Overview

2011 ◽  
pp. 1805-1811
Author(s):  
Alan Wee-Chung Liew ◽  
Ngai-Fong Law
Keyword(s):  
Image Compression ◽  
Basic Principle ◽  
Image Representation ◽  
Digital Camera ◽  
Image Features ◽  
Natural Image ◽  
Jpeg 2000 ◽  
Line Drawings ◽  
Core Technology ◽  
Lower Entropy

Image compression aims to produce a new image representation that can be stored and transmitted efficiently. It is a core technology for multimedia processing and has played a key enabling role in many commercial products, such as digital camera and camcorders. It facilitates visual data transmission through the Internet, contributes to the advent of digital broadcast system, and makes possible the storage on VCD and DVD. Despite a continuing increase in capacity, efficient transmission and storage of images still present the utmost challenge in all these systems. Consequently, fast and efficient compression algorithms are in great demand. The basic principle for image compression is to remove any redundancy in image representation. For example, simple graphic images such as icons and line drawings can be represented more efficiently by considering differences among neighbor pixels, as the differences always have lower entropy value than the original images (Shannon, 1948). These kinds of techniques are often referred to as lossless compression. It tries to exploit statistical redundancy in an image so as to provide a concise representation in which the original image can be reconstructed perfectly. However, statistical compression techniques alone cannot provide high compression ratio. To improve image compressibility, lossy compression is often used so that visually important image features are preserved while some fine details are removed or not represented perfectly. This type of compression is often used for natural images where the loss of some details is generally unnoticeable to viewers. This articles deals with image compression. Specifi- cally, it is concern with compression of natural color images because they constitute the most important class of digital image. First, the basic principle and methodology of natural image compression is described. Then, several major natural image compression standards, namely JPEG, JPEG-LS, and JPEG 2000 are discussed.

Natural Image Classification Based on Improved Support Vector Machine

2011 ◽  
Vol 58-60 ◽  
pp. 2387-2391
Author(s):  
Ying Jian Qi ◽  
Zhi Wei Ou ◽  
Bin Zhang ◽  
Ting Zhan Liu ◽  
Ying Li
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Ant Colony Algorithm ◽  
Image Representation ◽  
Image Features ◽  
Ant Colony ◽  
Function Parameter ◽  
Support Vector ◽  
Natural Image ◽  
Improved Support Vector Machine

Local image representation based natural image classification is an important task. SIFT descriptors and bag-of-visterm (BOV)method have achieved very good results. Many studies focused on improving the representation of the image, and then use the support vector machine to classify and identify the image category. However, due to support vector machine its own characteristics, it shows inflexible and slower convergence rate for large samples，with the selection of parameters influencing the results for the algorithm very much. Therefore, this paper will use the improved support vector machine algorithm be based on ant colony algorithm in classification step. The method adopt dense SIFT descriptors to describe image features and then use two levels BOV method to obtain the image representation. In recognition step, we use the support vector machine as a classifier but ant colony optimization method is used to selects kernel function parameter and soft margin constant C penalty parameter. Experiment results show that this solution determined the parameter automatically without trial and error and improved performance on natural image classification tasks.

Gender classification from offline multi-script handwriting images using oriented Basic Image Features (oBIFs)

2018 ◽  
Vol 99 ◽  
pp. 155-167 ◽  
Author(s):  
Abdeljalil Gattal ◽  
Chawki Djeddi ◽  
Imran Siddiqi ◽  
Youcef Chibani
Keyword(s):  
Image Features ◽  
Gender Classification ◽  
Basic Image

scholarly journals Investigation on Intelligent Recognition System of Instrument Based on Multi-step Convolution Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 185-192
Author(s):  
Feng Shan ◽  
◽  
Hui Sun ◽  
Xiaoyun Tang ◽  
Weiwei Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Recognition Rate ◽  
Test Site ◽  
Recognition System ◽  
Image Features ◽  
Convolution Neural Network ◽  
Digital Display ◽  
Industrial Control ◽  
Intelligent Recognition

Digital instruments are widely used in industrial control, traffic, equipment displays and other fields because of the intuitive characteristic of their test data. Aiming at the character recognition scene of digital display Vernier caliper, this paper creatively proposes an intelligent instrument recognition system based on multi-step convolution neural network (CNN). Firstly, the image smples are collected from the Vernier caliper test site, and their resolution and size are normalized. Then the CNN model was established to train the image smples and extract the features. The digital display region in the image smples were extracted according to the image features, and the numbers in the Vernier caliper were cut out. Finally, using the MINIST datas set of Vernier caliper is established, and the CNN model is used to recognize it. The test results show that the overall recognition rate of the proposed CNN model is more than 95%, and has good robustness and generalization ability.

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