Diagonal based feature extraction for handwritten character recognition system using neural network

2011 ◽  
Author(s):  
J. Pradeep ◽  
E. Srinivasan ◽  
S. Himavathi
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Character Recognition ◽  
Recognition System ◽  
Handwritten Character Recognition ◽  
Handwritten Character

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

scholarly journals Handwritten Character Recognition using Deep Learning and Neural Network

2021 ◽  
pp. 371-374
Author(s):  
Nikita Aware ◽  
Ashwini Bhagat ◽  
Komal Ghorpade ◽  
Komal Kerulkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Character Recognition ◽  
Recognition System ◽  
Handwritten Character Recognition ◽  
Query Image ◽  
Research Areas ◽  
Handwritten Character ◽  
Challenging Research ◽  
The Neural Networks

Handwritten character recognition is among the most challenging research areas in pattern recognition and image processing. With everything going digital, applications of handwritten character recognition are emerging in different offices, educational institutes, healthcare units, commercial units and banks etc., where the documents that are handwritten are dealt more frequently. Many researchers have worked with recognition of characters of different languages but there is comparatively less work carried for Devanagari Script. In past few years, however the work carried out in this direction is increasing to a great extent. Handwritten Devanagari Character Recognition is more challenging in comparison to the recognition of the Roman characters. The complexity is mostly due to the presence of a header line known as shirorekha that connects the Devanagari characters to form a word. The presence of this header line makes the segmentation process of characters more difficult. There is uniqueness to the handwriting styles of every individual which adds to the complexity. In this paper, a recognition system based on neural network has been proposed for Devanagari (Marathi) alphabets. Each of the characters that are extracted through query image is resized and is then passed to the neural networks for the process of recognition.

scholarly journals Diagonal Based Feature Extraction and Backpropagation Neural Network in Handwritten Batak Toba Characters Recognition

2021 ◽  
Author(s):  
Elviawaty Muisa Zamzami ◽  
Septi Hayanti ◽  
Erna Budhiarti Nababan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Character Recognition ◽  
Complex Problem ◽  
Backpropagation Neural Network ◽  
Training Process ◽  
Handwritten Character Recognition ◽  
Handwritten Character

Handwritten character recognition is considered a complex problem since one’s handwritten character has its characteristics.  Data used for this research was a photo of handwritten or scanned handwritten.  In this research, Backpropagation Neural Network (BPNN) was used to recognize handwritten Batak Toba character, wherein preprocessing stage feature extraction was done using Diagonal Based Feature Extraction (DBFE) to obtain feature value.  Furthermore, the feature value will be used as an input to BPNN. The total number of data used was190 data, where 114 data was used for the training process and another 76 data was used for testing. From the testing process carried out, the accuracy obtained was 87,19 %.

scholarly journals HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS

2021 ◽  
Vol 10 (8) ◽  
pp. 41-45
Author(s):  
D.J. Samatha Naidu ◽  
T. Mahammad Rafi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Character Recognition ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Recognition System ◽  
Self Organizing Map ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
The Neural Network

Handwritten character Recognition is one of the active area of research where deep neural networks are been utilized. Handwritten character Recognition is a challenging task because of many reasons. The Primary reason is different people have different styles of handwriting. The secondary reason is there are lot of characters like capital letters, small letters & special symbols. In existing were immense research going on the field of handwritten character recognition system has been design using fuzzy logic and created on VLSI(very large scale integrated)structure. To Recognize the tamil characters they have use neural networks with the Kohonen self-organizing map(SOM) which is an unsupervised neural networks. In proposed system this project design a image segmentation based hand written character recognition system. The convolutional neural network is the current state of neural network which has wide application in fields like image, video recognition. The system easily identify or easily recognize text in English languages and letters, digits. By using Open cv for performing image processing and having tensor flow for training the neural network. To develop this concept proposing the innovative method for offline handwritten characters. detection using deep neural networks using python programming language.

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