scholarly journals Probabilistic Neural Network based Approach for Handwritten Character Recognition

2010 ◽  
pp. 105-109
Author(s):  
V.N. Manjunath Aradhya ◽  
S. K. Niranjan ◽  
G. Hemantha Kumar
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Character Recognition ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Recognition System ◽  
Indian Language ◽  
South Indian ◽  
Extraction Scheme ◽  
Standard Techniques

In this paper, recognition system for totally unconstrained handwritten characters for south Indian language of Kannada is proposed. The proposed feature extraction technique is based on Fourier Transform and well known Principal Component Analysis (PCA). The system trains the appropriate frequency band images followed by PCA feature extraction scheme. For subsequent classification technique, Probabilistic Neural Network (PNN) is used. The proposed system is tested on large database containing Kannada characters and also tested on standard COIL-20 object database and the results were found to be better compared to standard techniques.

scholarly journals KANNADA CHARACTER RECOGNITION SYSTEM USING NEURAL NETWORK

2012 ◽  
pp. 206-208
Author(s):  
SURESH KUMAR D S ◽  
AJAY KUMAR B R ◽  
K SRINIVASA KALYAN
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Indian Language ◽  
Research Areas ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
Hidden Layer

Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form[1]. As there are no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India[2].In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten kannada character is resized into 20x30 pixel.The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

Devanagari Handwritten Marathi Compound Character Recognition System

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Chitra Bhole
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Character Recognition ◽  
Wavelet Transformation ◽  
Recognition System ◽  
Computational Time ◽  
Deep Convolution Neural Network ◽  
Time Required

Handwritten character recognition a field of research in AI, computer vision, and pattern recognition. Devanagari handwritten Marathi compound character recognition is most tedious tasks because of its complexity as compared to other languages. As compound character is combination of two or more characters it becomes challenging task to recognize it. However, the researchers used various methods like Neural Network, SVM, KNN, Wavelet transformation to classify the features of compound Marathi characters and tried to give the accuracy in the recognition of it. But the problem of feature extraction, and time required is large. In this paper I am proposing the Offline handwritten Marathi compound character recognition using deep convolution neural network which reduces the computational time and increases the accuracy.

FEATURE EXTRACTION IN CHARACTER RECOGNITION WITH ASSOCIATIVE MEMORY CLASSIFIER

1996 ◽  
Vol 10 (04) ◽  
pp. 325-348 ◽  
Author(s):  
MING ZHANG ◽  
CHING Y. SUEN ◽  
TIEN D. BUI
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Associative Memory ◽  
Data Reduction ◽  
Character Recognition ◽  
Recognition System ◽  
Extraction Process ◽  
The Neural Network ◽  
Series Of Experiments

A pattern recognition system mainly contains two functional parts, i.e. feature extraction and pattern classification. The success of such a system depends on not only the effectiveness of each of them, but also their operation in concert. The feature extraction process in a traditional recognition system has two major tasks, namely, to extract deformation invariant signals and to reduce data. When a neural network is used as a pattern classifier, however, an alteration in these basic objectives is needed. In particular, the consideration of data reduction will be replaced by that of the suitability of feature vectors to the neural network. In this paper, feature extraction algorithms in character recognition have been designed based on these principles. The improvements made by these algorithms have been demonstrated in a series of experiments which justify such a change in the fundamental objectives of the feature extraction process when an associative memory classifier is used.

scholarly journals Classification of malignant and Benign Lung Using Probabilistic Neural Network

2019 ◽  
pp. 307-311
Author(s):  
Asmitha Shree R ◽  
Sajitha M ◽  
Subha S
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Classification Performance ◽  
Cancer Types ◽  
Extraction Stage

Lung Cancer is considered as one of the deadliest diseases among other lung disorders and cancer types and is the leading cause of cancer deaths worldwide. Lung cancer is a curable disease if detected in its early stages that makes up 13% of all cancer diagnoses and 27% of all cancer deaths. The objective of this paper is mainly focused on categorizing the patients Computed Tomography (CT) lung images as normal or abnormal. The images are subjected to segmentation to focus on detecting the cancerous region to classify. Effective feature selection and feature extraction is made by applying Watershed Transform and Principal Component Analysis. The emphasis is on the feature extraction stage to yield a better classification performance. The classification of CT images as benign or malignant is done using Machine Learning based Neural Network.

scholarly journals Neural Network based Handwritten Character Recognition system

2018 ◽  
Vol 7 (03) ◽  
pp. 23761-23768 ◽  
Author(s):  
Savitha Attigeri
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Character Recognition ◽  
Recognition System ◽  
Feed Forward Neural Network ◽  
Handwritten Character Recognition ◽  
Data Set ◽  
Research Areas ◽  
Handwritten Character ◽  
The Neural Network

Handwritten character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network. Each character data set contains 26 alphabets. Fifty different character data sets are used for training the neural network. The trained network is used for classification and recognition. In the proposed system, each character is resized into 30x20 pixels, which is directly subjected to training. That is, each resized character has 600 pixels and these pixels are taken as features for training the neural network. The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition

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. 

Sign in / Sign up

Export Citation Format

Share Document