Performance analysis of Handwritten Devnagari Character Recognition using Feed Forward , Radial Basis , Elman Back Propagation, and Pattern Recognition Neural Network Model Using Different Feature Extraction Methods

This paper describes the performance analysis for the four types of neural network with different feature extraction methods for character recognition of hand written devnagari alphabets. We have implemented four types of networks i.e. Feed forward , Radial basis, Elman back propagation and Pattern recognition neural network using three different types of feature extraction methods i.e. pixel value, histogram and blocks mean for each network. These algorithms have been performed better than the conventional approaches of neural network for pattern recognition. It has been analyzed that the Radial Basis neural network performs better compared to other types of networks

PUBLIC OCR SIGN AGE RECOGNITION WITH SKEW & SLANT CORRECTION FOR VISUALLY IMP AIRED PEOPLE

This paper presents an OCR hybrid recognition model for the Visually Impaired People (VIP). The VIP often encounters problems navigating around independently because they are blind or have poor vision. They are always being discriminated due to their limitation which can lead to depression to the VIP. Thus, they require an efficient technological assistance to help them in their daily activity. The objective of this paper is to propose a hybrid model for Optical Character Recognition (OCR) to detect and correct skewed and slanted character of public signage. The proposed hybrid model should be able to integrate with speech synthesizer for VIP signage recognition. The proposed hybrid model will capture an image of a public signage to be converted into machine readable text in a text file. The text will then be read by a speech synthesizer and translated to voice as the output. In the paper, hybrid model which consist of Canny Method, Hough Transformation and Shearing Transformation are used to detect and correct skewed and slanted images. An experiment was conducted to test the hybrid model performance on 5 blind folded subjects. The OCR hybrid recognition model has successfully achieved a Recognition Rate (RR) of 82. 7%. This concept of public signage recognition is being proven by the proposed hybrid model which integrates OCR and speech synthesizer

SILECT: System of Interactive Education for Learning and Earning using Computer Technology

System of Interactive Education for Learning and Earning using Computer Technology (SILECT) is an effective yet simplistic Information Communication and Technology (ICT) system that is proposed with the clear intention to teach numerals to marginalized group of people of developing world. The project develops on further based on four major underlying concepts namely learning by teaching theories; operant conditioning theories; neural network theories; people and participation concepts; and last but not the least Trainees as Trainers Model (TasT). After learning numerals as a preliminary phase, which supports the earning phase, trainees are capable of teaching others using the SILECT tool

PUBLIC OCR SIGN AGE RECOGNITION WITH SKEW & SLANT CORRECTION FOR VISUALLY IMP AIRED PEOPLE

This paper presents an OCR hybrid recognition model for the Visually Impaired People (VIP). The VIP often encounters problems navigating around independently because they are blind or have poor vision. They are always being discriminated due to their limitation which can lead to depression to the VIP. Thus, they require an efficient technological assistance to help them in their daily activity. The objective of this paper is to propose a hybrid model for Optical Character Recognition (OCR) to detect and correct skewed and slanted character of public signage. The proposed hybrid model should be able to integrate with speech synthesizer for VIP signage recognition. The proposed hybrid model will capture an image of a public signage to be converted into machine readable text in a text file. The text will then be read by a speech synthesizer and translated to voice as the output. In the paper, hybrid model which consist of Canny Method, Hough Transformation and Shearing Transformation are used to detect and correct skewed and slanted images. An experiment was conducted to test the hybrid model performance on 5 blind folded subjects. The OCR hybrid recognition model has successfully achieved a Recognition Rate (RR) of 82. 7%. This concept of public signage recognition is being proven by the proposed hybrid model which integrates OCR and speech synthesizer

PUBLIC OCR SIGN AGE RECOGNITION WITH SKEW & SLANT CORRECTION FOR VISUALLY IMP AIRED PEOPLE

This paper presents an OCR hybrid recognition model for the Visually Impaired People (VIP). The VIP often encounters problems navigating around independently because they are blind or have poor vision. They are always being discriminated due to their limitation which can lead to depression to the VIP. Thus, they require an efficient technological assistance to help them in their daily activity. The objective of this paper is to propose a hybrid model for Optical Character Recognition (OCR) to detect and correct skewed and slanted character of public signage. The proposed hybrid model should be able to integrate with speech synthesizer for VIP signage recognition. The proposed hybrid model will capture an image of a public signage to be converted into machine readable text in a text file. The text will then be read by a speech synthesizer and translated to voice as the output. In the paper, hybrid model which consist of Canny Method, Hough Transformation and Shearing Transformation are used to detect and correct skewed and slanted images. An experiment was conducted to test the hybrid model performance on 5 blind folded subjects. The OCR hybrid recognition model has successfully achieved a Recognition Rate (RR) of 82. 7%. This concept of public signage recognition is being proven by the proposed hybrid model which integrates OCR and speech synthesizer