scholarly journals A Neural Network Based Artificial Vision System for Licence Plate Recognition

1997 ◽  
Vol 08 (01) ◽  
pp. 113-126 ◽  
Author(s):  
Sorin Draghici
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Vision System ◽  
High Reliability ◽  
Artificial Vision ◽  
Training Algorithms ◽  
Real World Data ◽  
Registration Number ◽  
Plate Location ◽  
High Level

This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solution used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%

Boosting and Other Ensemble Methods

1994 ◽  
Vol 6 (6) ◽  
pp. 1289-1301 ◽  
Author(s):  
Harris Drucker ◽  
Corinna Cortes ◽  
L. D. Jackel ◽  
Yann LeCun ◽  
Vladimir Vapnik
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Computational Cost ◽  
Training Algorithms ◽  
Surprising Result ◽  
Training Set ◽  
Set Size ◽  
Ensemble Techniques ◽  
Training Error

We compare the performance of three types of neural network-based ensemble techniques to that of a single neural network. The ensemble algorithms are two versions of boosting and committees of neural networks trained independently. For each of the four algorithms, we experimentally determine the test and training error curves in an optical character recognition (OCR) problem as both a function of training set size and computational cost using three architectures. We show that a single machine is best for small training set size while for large training set size some version of boosting is best. However, for a given computational cost, boosting is always best. Furthermore, we show a surprising result for the original boosting algorithm: namely, that as the training set size increases, the training error decreases until it asymptotes to the test error rate. This has potential implications in the search for better training algorithms.

FPGA Implementation of Decision Trees and Tree Ensembles for Character Recognition in Vivado Hls

2014 ◽  
Vol 19 (2-3) ◽  
pp. 71-82 ◽  
Author(s):  
Rafał Kułaga ◽  
Marek Gorgoń
Keyword(s):  
Decision Trees ◽  
Character Recognition ◽  
Data Transfer ◽  
Processing System ◽  
Fpga Implementation ◽  
Programmable Logic ◽  
Training Algorithms ◽  
Digit Recognition ◽  
Machine Learning Methods ◽  
High Level

Abstract Decision trees and decision tree ensembles are popular machine learning methods, used for classification and regression. In this paper, an FPGA implementation of decision trees and tree ensembles for letter and digit recognition in Vivado High-Level Synthesis is presented. Two publicly available datasets were used at both training and testing stages. Different optimizations for tree code and tree node layout in memory are considered. Classification accuracy, throughput and resource usage for different training algorithms, tree depths and ensemble sizes are discussed. The correctness of the module’s operation was verified using C/RTL cosimulation and on a Zynq-7000 SoC device, using Xillybus IP core for data transfer between the processing system and the programmable logic.

HANDWRITTEN NUMERAL AND MACHINE PRINTED MULTIPLE FONT CHARACTER RECOGNITION USING NEURAL NETWORK CLASSIFIER

1996 ◽  
Vol 06 (06) ◽  
pp. 569-580 ◽  
Author(s):  
J. CAO ◽  
M. AHMADI ◽  
M. SHRIDHAR
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Large Set ◽  
Real World Data ◽  
Neural Network Learning ◽  
Chain Code ◽  
Network Learning ◽  
Convergence Problems

In this paper a new neural network is proposed for recognition of handwritten digits and multi-font machine printed characters. In this system, overlapped regional chain code histograms of characters are used as features and a neural network has been used for classification. A new neural network learning algorithm that combines unsupervised learning with supervised learning has been developed. This new algorithm overcomes the slow learning and difficult convergence problems that are typical of back-propagation learning algorithms. The algorithm was tested on a large set of handwritten digits collected from real world data and a set of multi-font machine printed English letters.

A High Performance Algorithm of Vehicle Plate Location and Recognition in Intelligent Transportation

2012 ◽  
Vol 253-255 ◽  
pp. 1443-1452
Author(s):  
Wan Jun Liu ◽  
Zhan Zhang ◽  
Wen Tao Jiang ◽  
Heng Yuan
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
High Performance ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
License Plate ◽  
Search Area ◽  
System A ◽  
Weight Value ◽  
Plate Location

For the problem of slow plate location, the low accuracy of information identifying in the intelligent transportation system, a new algorithm of vehicle plate location and character recognition with high performance is presented in this paper. Firstly, the vehicle plate image is pre-processed, and then search for the edge of rapidly protruding rectangular, collect the similar trend of bilateral curve in the search area of rectangular, then selected the area with outstanding bilateral similar characteristics, so locate the true license plate which contains characters. Finally, match with the bit weight value of the multi-template by the improving neural network model, and identify the characters. The proposed algorithm focuses on the rectangular features of license plates and the important features of parallel bilateral with character, to the use of algorithm with the new parallel ectopic template removed improved the speed of vehicle plate location and the accuracy of character recognition.

scholarly journals AI-based localization and classification of skin disease with erythema

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ha Min Son ◽  
Wooho Jeon ◽  
Jinhyun Kim ◽  
Chan Yeong Heo ◽  
Hye Jin Yoon ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Diseases ◽  
Classification Model ◽  
Screening Tests ◽  
Sensitivity Score ◽  
Common Skin ◽  
Novel Method ◽  
Improved Performance ◽  
High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

scholarly journals Application of an Artificial Neural Network to Automate the Measurement of Kinematic Characteristics of Punches in Boxing

2021 ◽  
Vol 11 (3) ◽  
pp. 1223
Author(s):  
Ilshat Khasanshin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kinematic Parameters ◽  
Constant Control ◽  
Measurement Units ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Effective Development ◽  
High Level ◽  
Different Levels

This work aimed to study the automation of measuring the speed of punches of boxers during shadow boxing using inertial measurement units (IMUs) based on an artificial neural network (ANN). In boxing, for the effective development of an athlete, constant control of the punch speed is required. However, even when using modern means of measuring kinematic parameters, it is necessary to record the circumstances under which the punch was performed: The type of punch (jab, cross, hook, or uppercut) and the type of activity (shadow boxing, single punch, or series of punches). Therefore, to eliminate errors and accelerate the process, that is, automate measurements, the use of an ANN in the form of a multilayer perceptron (MLP) is proposed. During the experiments, IMUs were installed on the boxers’ wrists. The input parameters of the ANN were the absolute acceleration and angular velocity. The experiment was conducted for three groups of boxers with different levels of training. The developed model showed a high level of punch recognition for all groups, and it can be concluded that the use of the ANN significantly accelerates the collection of data on the kinetic characteristics of boxers’ punches and allows this process to be automated.

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