Recognizing Check Magnetic Code Based on Peak-Valley Code and Distance

2011 ◽  
Vol 145 ◽  
pp. 588-592
Author(s):  
Di Fan ◽  
Jia Li ◽  
Mao Yong Cao ◽  
Nong Liang Sun ◽  
Hong Yu
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Computational Cost ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Statistical Parameters ◽  
Neighbor Recognition ◽  
Cash Payment ◽  
Low Computational Cost

The check is a popular form for the non-cash payment and accounts for more than 50% of the non-cash transactions. Magnetic ink character recognition system can recognize the check magnetic code automatically and get the information of the bank and account. In magnetic ink character recognition system, the recognizing algorithm is mostly based on correlation coefficient. The computational cost of this algorithm is very high. This paper has proposed a new algorithm based on the peak-valley code and peak-valley distance after analyzing the characteristics of magnetic code signals in E-13B standards to simplify the calculations and system design. Firstly, the magnetic code signal is normalized and separated into magnetic character signals by the thresholds of peak and valley. Secondly, the features of the peak-valley code and peak-valley distance of each magnetic character signal are extracted, then the recognition based on peak-valley code and the nearest neighbor recognition algorithm based on peak-valley distance are utilized to recognize the magnetic code. The recognition results and statistical parameters from a large number of experiments show that the new method has high recognition rate, good robustness and low computational cost.

Recognizing Check Magnetic Code Based on Peak-Valley Location and Amplitude

2012 ◽  
Vol 249-250 ◽  
pp. 241-246
Author(s):  
Di Fan ◽  
Zhe Chen ◽  
Chang Cun Bu ◽  
Zhun Sheng Yang ◽  
Jia Li
Keyword(s):  
System Design ◽  
Correlation Coefficient ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
New Method ◽  
Statistical Parameters ◽  
Computation Cost ◽  
Significant Time ◽  
Neighbor Recognition

Magnetic code is widely used in check, securities, tax invoice, etc. However, the traditional recognizing and reading method of magnetic code is mostly based on correlation coefficient and it takes significant time and cost. After analyzing the characteristics of magnetic code signals in E-13B standard, this paper has proposed a new algorithm based on the peak-valley location and amplitude (PVLA) to simplify the calculation and system design. Firstly, the magnetic code signal is separated into magnetic ink character signals by the thresholds of peak and valley. Secondly, the features of the peak-valley location (PVL) and peak-valley amplitude(PVA) of each magnetic ink character signal are extracted and normalized, then the nearest neighbor recognition algorithm based on the vectors of peak-valley location and amplitude is utilized to recognize the magnetic code. The recognition results and statistical parameters from a large number of experiments show that the new method has higher recognition rate and better robustness. In addition, the new algorithm only involves additions and subtractions, so it has a lower computation cost.

Design of Gait Recognition System Based on FPGA and DSP

2014 ◽  
Vol 687-691 ◽  
pp. 3861-3868
Author(s):  
Zheng Hong Deng ◽  
Li Tao Jiao ◽  
Li Yan Liu ◽  
Shan Shan Zhao
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Zernike Moment ◽  
Intelligent Monitoring ◽  
The One

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

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.

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.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

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%.

A Prototype for Brazilian Bankcheck Recognition

1997 ◽  
Vol 11 (04) ◽  
pp. 549-569 ◽  
Author(s):  
Luan L. Lee ◽  
Miguel G. Lizarraga ◽  
Natanael R. Gomes ◽  
Alessandro L. Koerich
Keyword(s):  
Information Extraction ◽  
Character Recognition ◽  
Type I Error ◽  
Recognition Rate ◽  
Simulated Data ◽  
Recognition System ◽  
Signature Verification ◽  
Type I ◽  
Verification Algorithm ◽  
Commercial Applications

This paper describes a prototype for Brazilian bankcheck recognition. The description is divided into three topics: bankcheck information extraction, digit amount recognition and signature verification. In bankcheck information extraction, our algorithms provide signature and digit amount images free of background patterns and bankcheck printed information. In digit amount recognition, we dealt with the digit amount segmentation and implementation of a complete numeral character recognition system involving image processing, feature extraction and neural classification. In signature verification, we designed and implemented a static signature verification system suitable for banking and commercial applications. Our signature verification algorithm is capable of detecting both simple, random and skilled forgeries. The proposed automatic bankcheck recognition prototype was intensively tested by real bankcheck data as well as simulated data providing the following performance results: for skilled forgeries, 4.7% equal error rate; for random forgeries, zero Type I error and 7.3% Type II error; for bankcheck numerals, 92.7% correct recognition rate.

scholarly journals Handwritten tifinagh character recognition using simple geometric shapes and graphs

2019 ◽  
Vol 13 (2) ◽  
pp. 598
Author(s):  
Youssef Ouadid ◽  
Abderrahmane Elbalaoui ◽  
Mehdi Boutaounte ◽  
Mohamed Fakir ◽  
Brahim Minaoui
Keyword(s):  
Character Recognition ◽  
Graph Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Features Extraction ◽  
Experimental Results ◽  
Feature Points ◽  
Matching Method ◽  
Extraction Algorithm ◽  
Novel Algorithm

<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>

scholarly journals Online Handwritten Gurmukhi Character Recognition using Hybrid Feature Set

2018 ◽  
Vol 7 (3.4) ◽  
pp. 90 ◽  
Author(s):  
Mandeep Singh ◽  
Karun Verma ◽  
Bob Gill ◽  
Ramandeep Kaur
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Classification Models ◽  
Stroke Classification ◽  
Online Recognition ◽  
The World ◽  
Gurmukhi Script ◽  
Online Handwriting ◽  
Natural Way

Online handwriting character recognition is gaining attention from the researchers across the world because with the advent of touch based devices, a more natural way of communication is being explored. Stroke based online recognition system is proposed in this paper for a very complex Gurmukhi script. In this effort, recognition for 35 basic characters of Gurmukhi script has been implemented on the dataset of 2019 Gurmukhi samples. For this purpose, 32 stroke classes have been considered. Three types of features have been extracted. Hybrid of these features has been proposed in this paper to train the classification models. For stroke classification, three different classifiers namely, KNN, MLP and SVM are used and compared to evaluate the effectiveness of these models. A very promising “stroke recognition rate” of 94% by KNN, 95.04% by MLP and 95.04% by SVM has been obtained.  

Probabilistic Model Based Hybrid Classifier for Character Recognition

2017 ◽  
Vol 25 (04) ◽  
pp. 621-647
Author(s):  
A. K. Sampath ◽  
N. Gomathi
Keyword(s):  
Probabilistic Model ◽  
Character Recognition ◽  
Recognition Rate ◽  
Size Variation ◽  
Orientation Angle ◽  
Recognition System ◽  
Input Image ◽  
Hybrid Classifier ◽  
Form Processing ◽  
Model Based

Handwritten character recognition is most crucial one indulging in many of the applications like forensic search, searching historical manuscripts, mail sorting, bank check reading, tax form processing, book and handwritten notes transcription etc. The problem occurrence in the recognition is mainly because of the writing style variation, size variation (length and height), orientation angle etc. In this paper a probabilistic model based hybrid classifier is proposed for the character recognition combining the neural network and decision tree classifiers. In addition to the local gradient features i.e. histogram oriented feature and grid level feature, an additional feature called GLCM feature is extracted from the input image in the proposed recognition system and are concatenated for the image recognition procedure to encode color, shape, texture, local as well as the statistical information. These extracted features considered are given to the hybrid classifier which recognises the character. In the test set, recognition accuracy of 95% is achieved. The proposed probabilistic model based hybrid classifier tends to contribute more accurate character recognition rate compared to the existing character recognition system.

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