Handwritten Character Recognition Based on a Multiple Fermat's Spiral

2013 ◽  
Vol 774-776 ◽  
pp. 1629-1635
Author(s):  
Aissa Boudjella ◽  
Brahim Belhouari Samir ◽  
Omar Kassem Khalil
Keyword(s):  
Character Recognition ◽  
High Performance ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Recognition System ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Knn Classifier ◽  
New Feature ◽  
Performance Character

This paper describes a new feature extraction method which can be used very effectively in combination with Cluster K-Nearest Neighbor (CKNN) and KNN Classifier for image recognition. We propose handwritten English character recognition using Fermat's spiral approach to convert an image space into a parameter space. The system is implemented and simulated in MATLAB, and its performance is tested on real alphabet handwriting image. Fifteen (15) alphabet classes were created to carry out the experiment. Each class contains 9 alphabets {a, b, c, d, e, f, g, h, i}. The total of 15x9=135 alphabet images are captured under fixed camera position and controlled energy light intensity. The experimental results give a better recognition rate, 76.19% for KNN and 95.16% for C-KNN with reducing the overall data size of the transformed image. The relationship between the accuracy and k is investigated. It seems that when k goes from 1 to 9, the accuracy decreases linearly. The result of this investigation is a high performance character recognition system with significantly improved recognition rates and real-time.

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

scholarly journals Optimization of deep learning features for age-invariant face recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 1833
Author(s):  
Amal A. Moustafa ◽  
Ahmed Elnakib ◽  
Nihal F. F. Areed
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
K Nearest Neighbor ◽  
Face Images ◽  
Knn Classifier ◽  
Deep Learning Features

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods.

scholarly journals Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data

2016 ◽  
Vol 13 (5) ◽  
Author(s):  
Malik Yousef ◽  
Waleed Khalifa ◽  
Loai AbdAllah
Keyword(s):  
High Performance ◽  
Nearest Neighbor ◽  
Ensemble Clustering ◽  
Distance Metric ◽  
K Nearest Neighbor ◽  
Clustering Ensemble ◽  
Data Mining Algorithms ◽  
Knn Classifier ◽  
Knn Classification ◽  
Mining Algorithms

SummaryThe performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

scholarly journals APPLICATION OF ZONAL AND CURVATURE FEATURES TO NUMERALS RECOGNITION

2021 ◽  
Vol 9 (2) ◽  
pp. 7-12
Author(s):  
Binod Kumar Prasad
Keyword(s):  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Signature Verification ◽  
Optical Character ◽  
Knn Classifier ◽  
Average Recognition Rate ◽  
Distance Coding

Purpose of the study: The purpose of this work is to present an offline Optical Character Recognition system to recognise handwritten English numerals to help automation of document reading. It helps to avoid tedious and time-consuming manual typing to key in important information in a computer system to preserve it for a longer time. Methodology: This work applies Curvature Features of English numeral images by encoding them in terms of distance and slope. The finer local details of images have been extracted by using Zonal features. The feature vectors obtained from the combination of these features have been fed to the KNN classifier. The whole work has been executed using the MatLab Image Processing toolbox. Main Findings: The system produces an average recognition rate of 96.67% with K=1 whereas, with K=3, the rate increased to 97% with corresponding errors of 3.33% and 3% respectively. Out of all the ten numerals, some numerals like ‘3’ and ‘8’ have shown respectively lower recognition rates. It is because of the similarity between their structures. Applications of this study: The proposed work is related to the recognition of English numerals. The model can be used widely for recognition of any pattern like signature verification, face recognition, character or word recognition in another language under Natural Language Processing, etc. Novelty/Originality of this study: The novelty of the work lies in the process of feature extraction. Curves present in the structure of a numeral sample have been encoded based on distance and slope thereby presenting Distance features and Slope features. Vertical Delta Distance Coding (VDDC) and Horizontal Delta Distance Coding (HDDC) encode a curve from vertical and horizontal directions to reveal concavity and convexity from different angles.

A Reliability Design Methodology for Chinese Character Recognition

1998 ◽  
Vol 12 (02) ◽  
pp. 159-172 ◽  
Author(s):  
Y. S. Huang ◽  
K. Liu ◽  
C. Y. Suen ◽  
Y. Y. Tang
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition System ◽  
Reliability Design ◽  
Chinese Character Recognition ◽  
Nearest Neighbor Rule ◽  
Pattern Distribution

This paper proposes a novel method which enables a Chinese character recognition system to obtain reliable recognition. In this method, two thresholds, i.e. class region thresholdRk and disambiguity thresholdAk, are used by each Chinese character k when the classifier is designed based on the nearest neighbor rule, where Rk defines the pattern distribution region of character k, and Ak prevents the samples not belonging to character k from being ambiguously recognized as character k. A novel algorithm to derive the appropriate thresholds Ak and Rk is developed so that a better recognition reliability can be obtained through iterative learning. Experiments performed on the ITRI printed Chinese character database have achieved highly reliable recognition performance (such as 0.999 reliability with a 95.14% recognition rate), which shows the feasibility and effectiveness of the proposed method.

scholarly journals Analysis of Different M-Band Wavelet Filters for Face Recognition using Nearest Neighbor Classifier

2018 ◽  
Vol 12 (2) ◽  
pp. 824
Author(s):  
C Hemalatha ◽  
E Logashanmugam
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Recognition System ◽  
K Nearest Neighbor ◽  
Wavelet Filters ◽  
Knn Classifier ◽  
Face Recognition System ◽  
The Face

<span>Face recognition system is one of the most interesting studied topics in computer vision for past two decades. Among the other popular biometrics such as the retina, fingerprint, and iris recognition systems, the face recognition is capable of recognizing the uncooperative samples in a non-intrusive manner. Also, it can be applied to many applications of surveillance security, forensics, border control, digital entertainment where face recognition is used in most. In the proposed system an automatic face recognition system is discussed. The proposed recognition system is based on the Dual-Tree M-Band Wavelet Transform (DTMBWT) transform algorithm and features obtained by varying the different filter in the DTMBWT transform. Then the different filter features are classified by means of the K-Nearest Neighbor (KNN) classifier for recognizing the face correctly. The implementation of the system is done by using the ORL face image database, and the performance metrics are calculated.</span>

scholarly journals Automated Car Number Plate Recognition System using K-Nearest Neighbor

2019 ◽  
Vol 8 (10) ◽  
pp. 1113-1117
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Morphological Operation ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Optical Character ◽  
Neighbor Classification

The Automatic number plate recognition (ANPR) is a mass reconnaissance strategy that utilizations optical character recognition on images to peruse the license plates on vehicles. The car number plate detection has the various phases like pre-processing, segmentation and classification. In the previous work, the morphological operation is applied for the car number plate detection. The morphological operation has the low accuracy for the car number plate detection. In the proposed work, the region based segmentation and K-nearest neighbor classification is applied for the character recognition. The proposed is implemented in MATLAB and results are analyzed in terms of accuracy.

scholarly journals Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

2018 ◽  
Vol 6 (4) ◽  
pp. 129-134 ◽  
Author(s):  
Jumoke Falilat Ajao ◽  
David Olufemi Olawuyi ◽  
Odetunji Ode Odejobi
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Chain Code ◽  
Nearest Neighbor Classifier ◽  
Digitized Images ◽  
Neighbor Classifier

This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.

scholarly journals Person Recognition by Hilbert Pair of Wavelets using Facial Images

2018 ◽  
Vol 7 (3) ◽  
pp. 1282
Author(s):  
Hemalatha C ◽  
Logashanmugam E
Keyword(s):  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Human Mind ◽  
K Nearest Neighbor ◽  
Person Recognition ◽  
Knn Classifier ◽  
Expected Performance ◽  
The Face ◽  
Facial Images

In human identification, the face acts as an important tool that carries the identity of each person. The human mind has the ability to re-cognize faces after the first view of a human face. Though there are many types of face detection/recognition system found no method can give the 100% accurate outputs. In this proposed system we are implementing and analyzing a new method that can be used for person recognition system that can produce better output accuracies. In the proposed system of person recognition method, one of the robust wavelet transform methods is used for the extraction of the features from the original images. The wavelet type used is known as the Dual Tree M-Band Wavelet Transform (DTMBWT) method. Using this transform the low and high sub-bands is obtained. These low and high sub-band coefficients are given as the input for the classification purpose. The sub-band obtained from the DTMBWT transform is given as the inputs for the classification purpose. The classification process is done using the K-Nearest Neighbor (KNN) classifier scheme. The system is implemented by using the facial images from the ORL database. By using this dataset images the performance measures of the proposed system is calculated in the form of graphical results such as Receiver Operating Characteristic (ROC), Inverse ROC and Expected Performance Curve (EPC) curves. Results show that proposed DTMBWT based face recognition provides better results than other approaches.  

R-HOG Feature-Based Off-Line Odia Handwritten Character Recognition

2020 ◽  
pp. 196-210 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Recognition System ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
Gradient Based ◽  
Feature Based ◽  
Handwritten Recognition ◽  
Fold Cross Validation

Offline handwritten recognition system for Odia characters has received attention in the last few years. Although the recent research showed that there has been lots of work reported in different language, there is limited research carried out in Odia character recognition. Most of Odia characters are round in nature, similar in orientation and size also, which increases the ambiguity among characters. This chapter has harnessed the rectangle histogram-oriented gradient (R-HOG) for feature extraction method along with the principal component analysis. This gradient-based approach has been able to produce relevant features of individual ones in to the proposed model and helps to achieve high recognition rate. After certain simulations, the respective analysis of classifier shows that SVM performed better than quadratic. Among them, SVM produces with 98.8% and QC produces 96.8%, respectively, as recognition rate. In addition to it, the authors have also performed the 10-fold cross-validation to make the system more robust.

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