A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

scholarly journals Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks

2018 ◽  
Vol 10 (7) ◽  
pp. 1123 ◽  
Author(s):  
Yuhang Zhang ◽  
Hao Sun ◽  
Jiawei Zuo ◽  
Hongqi Wang ◽  
Guangluan Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Loss Function ◽  
Extraction Method ◽  
Generative Adversarial Networks ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Adversarial Networks ◽  
Aircraft Type

Aircraft type recognition plays an important role in remote sensing image interpretation. Traditional methods suffer from bad generalization performance, while deep learning methods require large amounts of data with type labels, which are quite expensive and time-consuming to obtain. To overcome the aforementioned problems, in this paper, we propose an aircraft type recognition framework based on conditional generative adversarial networks (GANs). First, we design a new method to precisely detect aircrafts’ keypoints, which are used to generate aircraft masks and locate the positions of the aircrafts. Second, a conditional GAN with a region of interest (ROI)-weighted loss function is trained on unlabeled aircraft images and their corresponding masks. Third, an ROI feature extraction method is carefully designed to extract multi-scale features from the GAN in the regions of aircrafts. After that, a linear support vector machine (SVM) classifier is adopted to classify each sample using their features. Benefiting from the GAN, we can learn features which are strong enough to represent aircrafts based on a large unlabeled dataset. Additionally, the ROI-weighted loss function and the ROI feature extraction method make the features more related to the aircrafts rather than the background, which improves the quality of features and increases the recognition accuracy significantly. Thorough experiments were conducted on a challenging dataset, and the results prove the effectiveness of the proposed aircraft type recognition framework.

Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach

2016 ◽  
pp. 321-346
Author(s):  
Bhuvaneswari Chandran ◽  
P. Aruna ◽  
D. Loganathan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Lung Diseases ◽  
Hybrid Genetic Algorithm ◽  
Extraction Methods ◽  
Disease Classification ◽  
Filter Method ◽  
Svm Classifier ◽  
Genetic Approach ◽  
Feature Extraction Method

The purpose of the chapter is to present a novel method to classify lung diseases from the computed tomography images which assist physicians in the diagnosis of lung diseases. The method is based on a new approach which combines a proposed M2 feature extraction method and a novel hybrid genetic approach with different types of classifiers. The feature extraction methods performed in this work are moment invariants, proposed multiscale filter method and proposed M2 feature extraction method. The essential features which are the results of the feature extraction technique are selected by the novel hybrid genetic algorithm feature selection algorithms. Classification is performed by the support vector machine, multilayer perceptron neural network and Bayes Net classifiers. The result obtained proves that the proposed technique is an efficient and robust method. The performance of the proposed M2 feature extraction with proposed hybrid GA and SVM classifier combination achieves maximum classification accuracy.

Performance Evaluation of MadBoost on Face Detection

2019 ◽  
Vol 892 ◽  
pp. 200-209
Author(s):  
Rayner Pailus ◽  
Rayner Alfred
Keyword(s):  
Feature Extraction ◽  
Face Detection ◽  
Recognition Performance ◽  
Principal Component ◽  
Haar Wavelet ◽  
Extraction Methods ◽  
Face Image ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Face Images

Adaboost Viola-Jones method is indeed a profound discovery in detecting face images mainly because it is fast, light and one of the easiest methods of detecting face images among other techniques of face detection. Viola Jones uses Haar wavelet filter to detect face images and it produces almost 80%accuracy of face detection. This paper discusses proposed methodology and algorithms that involved larger library of filters used to create more discrimination features among the images by processing the proposed 15 Haar rectangular features (an extension from 4 Haar wavelet filters of Viola Jones) and used them in multiple adaptive ensemble process of detecting face image. After facial detection, the process continues with normalization processes by applying feature extraction such as PCA combined with LDA or LPP to extract our week learners’ wavelet for more classification features. Upon the process of feature extraction proposed feature selection to index these extracted data. These extracted vectors are used for training and creating MADBoost (Multiple Adaptive Diversified Boost)(an improvement of Adaboost, which uses multiple feature extraction methods combined with multiple classifiers) is able to capture, recognize and distinguish face image (s) faster. MADBoost applies the ensemble approach with better weights for classification to produce better face recognition results. Three experiments have been conducted to investigate the performance of the proposed MADBoost with three other classifiers, Neural Network (NN), Support Vector Machines (SVM) and Adaboost classifiers using Principal Component Analysis (PCA) as the feature extraction method. These experiments were tested against obstacles of POIES (Pose, Obstruction, Illumination, Expression, Sizes). Based on the results obtained, Madboost is found to be able to improve the recognition performance in matching failures, incorrect matching, matching success percentages and acceptable time taken to perform the classification task.

scholarly journals Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 843
Author(s):  
Md. Johirul Islam ◽  
Shamim Ahmad ◽  
Fahmida Haque ◽  
Mamun Bin Ibne Reaz ◽  
Mohammad Arif Sobhan Bhuiyan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Spatial Correlation ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Nonlinear Transformation ◽  
Computational Time ◽  
Feature Extraction Method ◽  
Invariant Feature ◽  
Upper Limb Prostheses

A force-invariant feature extraction method derives identical information for all force levels. However, the physiology of muscles makes it hard to extract this unique information. In this context, we propose an improved force-invariant feature extraction method based on nonlinear transformation of the power spectral moments, changes in amplitude, and the signal amplitude along with spatial correlation coefficients between channels. Nonlinear transformation balances the forces and increases the margin among the gestures. Additionally, the correlation coefficient between channels evaluates the amount of spatial correlation; however, it does not evaluate the strength of the electromyogram signal. To evaluate the robustness of the proposed method, we use the electromyogram dataset containing nine transradial amputees. In this study, the performance is evaluated using three classifiers with six existing feature extraction methods. The proposed feature extraction method yields a higher pattern recognition performance, and significant improvements in accuracy, sensitivity, specificity, precision, and F1 score are found. In addition, the proposed method requires comparatively less computational time and memory, which makes it more robust than other well-known feature extraction methods.

scholarly journals A Novel 2D Feature Extraction Method for Fingerprints Using Minutiae Points and Their Intersections

2017 ◽  
Vol 7 (5) ◽  
pp. 2547
Author(s):  
Nibras Ar Rakib ◽  
SM Zamshed Farhan ◽  
Md Mashrur Bari Sobhan ◽  
Jia Uddin ◽  
Arafat Habib
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Fingerprint Recognition ◽  
Support Vector ◽  
Svm Classifier ◽  
Fingerprint Image ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
Recognition Systems ◽  
To Come

The field of biometrics has evolved tremendously for over the last century. Yet scientists are still continuing to come up with precise and efficient algorithms to facilitate automatic fingerprint recognition systems. Like other applications, an efficient feature extraction method plays an important role in fingerprint based recognition systems. This paper proposes a novel feature extraction method using minutiae points of a fingerprint image and their intersections. In this method, initially, it calculates the ridge ends and ridge bifurcations of each fingerprint image. And then, it estimates the minutiae points for the intersection of each ridge end and ridge bifurcation. In the experimental evaluation, we tested the extracted features of our proposed model using a support vector machine (SVM) classifier and experimental results show that the proposed method can accurately classify different fingerprint images.

scholarly journals Classification of Multi-spectral Landsat Satellite Imagery Dataset using Feature Extraction Methods

2020 ◽  
Vol 9 (4) ◽  
Keyword(s):  
Land Use ◽  
Feature Extraction ◽  
Satellite Imagery ◽  
Extraction Method ◽  
Spatial Information ◽  
Extraction Methods ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Landsat Satellite ◽  
Spatial Maps

The feature extraction of multi-spectral Landsat satellite imagery Dataset is essential for vegetation monitoring, urban planning, change assessment, and other land-use applications. The spatial information provided by Remote sensing satellite imagery data is helpful for planning and decision-making policies. In the present study, classify the features of the multi-spectral Landsat satellite imagery dataset in different periods using the feature extraction method, and is produced the spatial maps of the study area. The study is to analyze the appropriate method of feature extraction for classifying the orchards, vegetation, rangeland, agricultural land, wetland, water body, and urban land using multi-temporal satellite dataset. In this study, use the three feature extraction methods are support vector machine (SVM), minimum distance (MD), and Maximum likelihood classifier (MLC) for supervised pixel-based classification using medium resolution (30 m) satellite dataset. The accuracy of feature extraction method is performed by the MLC (86.29% and 93% in the year 2003 and 2017) and SVM (86.37% and 90% in the year 2003 and 2017). The result of the presented study shows MLC and SVM classifier performs similar results but better than MD classifier for land-use/cover features classification. The classified spatial maps provide the essential spatial information for land-use changes occurred during the last 15 years (2003 to 2017).

scholarly journals Handwritten Digit Recognition from Digital Image

2019 ◽  
Vol 8 (12) ◽  
pp. 2389-2394
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Feature Extraction Method ◽  
Digit Recognition ◽  
Individual Character ◽  
Handwritten Digit ◽  
Character Classification ◽  
Straight Lines

This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) digit/chratchter. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the digit/characters, such as solid binary characters, skeletons (thinned digit /characters), or gray level sub images of each individual character. Latest research in this area has been able to grown some new methodologies to overcome the complexity of Guajarati digit writing style. The recognition of handwritten digits which are written in proper way to easily readable. The problem is human can write digit in different styles so it is not identified by the computer but the some feature extraction methodologies like end point, junction point; straight lines etc. For features identification and character classification studied different algorithm and technique

scholarly journals A Sparse Feature Extraction Method with Elastic Net for Drug-Target Interaction Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zheng-Yang Zhao ◽  
Wen-Zhun Huang ◽  
Jie Pan ◽  
Yu-An Huang ◽  
Shan-Wen Zhang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Drug Target ◽  
Elastic Net ◽  
Support Vector ◽  
Svm Classifier ◽  
Rotation Forest ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
Comparison Results

The identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. However, the traditional high-throughput techniques based on clinical trials are costly, cumbersome, and time-consuming for identifying DTIs. Hence, new intelligent computational methods are urgently needed to surmount these defects in predicting DTIs. In this paper, we propose a novel computational method that combines position-specific scoring matrix (PSSM), elastic net based sparse features extraction, and rotation forest (RF) classifier. Specifically, we converted each protein primary sequence into PSSM, which contains biological evolutionary information. Then we extract the hidden sparse feature descriptors in PSSM by elastic net based sparse feature extraction method (ESFE). After that, we fuse them with the features of drug, which are represented by molecular fingerprints. Finally, rotation forest classifier works on detecting the potential drug-target interactions. When performing the proposed method by the experiments of fivefold cross validation (CV) on enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor datasets, this method achieves average accuracies of 90.32%, 88.91%, 80.65%, and 79.73%, respectively. We also compared the proposed model with the state-of-the-art support vector machine (SVM) classifier and other effective methods on the same datasets. The comparison results distinctly indicate that the proposed model possesses the efficient and robust ability to predict DTIs. We expect that the new model will be able to take effects on predicting massive DTIs.

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