Rearrangement of Coordinate Selection for Triangle Features Improvement in Digit Recognition

Triangle geometry feature demonstrated as useful properties in classifying the image. This feature has been implemented in numerous recognition field such as biometric area, security area, medical area, geological area, inspection area and digit recognition area. This study is focusing on improving triangle features in digit recognition. Commonly, triangle features are explored by determining three points of triangle shape which represent as A, B and C to extract useful features in digit recognition. There is possibilities triangle shape cannot be formed when chosen coordinate are in line. Thus, a prior study has proposed an improvement on triangle selection point technique by determining the position of coordinate A, B and C use gradient value to identify the triangle shape can be modelled or vice versa. The suggested improvement is based on the dominant distribution which only covers certain areas of an image. Hence, a method named Triangle Point using Three Block (Tp3B) was proposed in this study. The proposed method proposes the arrangement of selection coordinate point based on three different blocks which where all coordinates points of an image were covered. Experiments have developed over image digit dataset of IFCHDB, HODA, MNIST and BANGLA which contains testing and train data of each. Features classification accuracy tested using supervised machine learning (SML) which is Support Vector Machine (SVM). Experimental results show, the proposed technique gives a promising result for dataset HODA and MNIST

Reconocimiento de notación matemática escrita a mano fuera de línea

El reconocimiento automático de expresiones matemáticas es uno de los problemas de reconocimiento de patrones, debido a que las matemáticas representan una fuente valiosa de información en muchos a ́reas de investigación. La escritura de expresiones matemáticas a mano es un medio de comunicación utilizado para la transmisión de información y conocimiento, con la cual se pueden generar de una manera sencilla escritos que contienen notación matemática. Este proceso puede volverse tedioso al ser escrito en lenguaje de composición tipográfica que pueda ser procesada por una computadora, tales como LATEX, MathML, entre otros. En los sistemas de reconocimiento de expresiones matem ́aticas existen dos m ́etodos diferentes a saber: fuera de l ́ınea y en l ́ınea. En esta tesis, se estudia el desempen ̃o de un sistema fuera de l ́ınea en donde se describen los pasos b ́asicos para lograr una mejor precisio ́n en el reconocimiento, las cuales esta ́n divididas en dos pasos principales: recono- cimiento de los s ́ımbolos de las ecuaciones matema ́ticas y el ana ́lisis de la estructura en que est ́an compuestos. Con el fin de convertir una expresi ́on matema ́tica escrita a mano en una expresio ́n equivalente en un sistema de procesador de texto, tal como TEX

Feature Extraction Methods for Character Recognition

Not Include

Implementation of a Modified Counterpropagation Neural Network Model in Online Handwritten Character Recognition System

Artificial neural networks are one of the widely used automated techniques. Though they yield high accuracy, most of the neural networks are computationally heavy due to their iterative nature. Hence, there is a significant requirement for a neural classifier which is computationally efficient and highly accurate. To this effect, a modified Counter Propagation Neural Network (CPN) is employed in this work which proves to be faster than the conventional CPN. In the modified CPN model, there was no need of training parameters because it is not an iterative method like backpropagation architecture which took a long time for learning. This paper implemented a modified Counterpropagation neural network for recognition of online uppercase (A-Z), lowercase (a-z) English alphabets and digits (0-9). The system is tested for different handwritten character samples and better recognition accuracies of 65% to 96% were obtained compared to related work in literature.   Keywords: Artificial Neural Network, Counterpropagation Neural Network, Character Recognition, Feature Extraction

High-Quality Wavelets Features Extraction for Handwritten Arabic Numerals Recognition

Arabic handwritten digit recognition is the science of recognition and classification of handwritten Arabic digits. It has been a subject of research for many years with rich literature available on the subject.  Handwritten digits written by different people are not of the same size, thickness, style, position or orientation. Hence, many different challenges have to overcome for resolving the problem of handwritten digit recognition.  The variation in the digits is due to the writing styles of different people which can differ significantly.  Automatic handwritten digit recognition has wide application such as automatic processing of bank cheques, postal addresses, and tax forms. A typical handwritten digit recognition application consists of three main stages namely features extraction, features selection, and classification. One of the most important problems is feature extraction. In this paper, a novel feature extraction approach for off-line handwritten digit recognition is presented. Wavelets-based analysis of image data is carried out for feature extraction, and then classification is performed using various classifiers. To further reduce the size of training data-set, high entropy subbands are selected. To increase the recognition rate, individual subbands providing high classification accuracies are selected from the over-complete tree. The features extracted are also normalized to standardize the range of independent variables before providing them to the classifier. Classification is carried out using k-NN and SVMs. The results show that the quality of extracted features is high as almost equivalently high classification accuracies are acquired for both classifiers, i.e. k-NNs and SVMs

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject