Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition

Geometry features has been widely used in image processing especially in face recognition, fingerprint recognition, digit recognition, vehicle detection and also in intrusion. Among the commonly used geometry features are the features that are based on triangle properties. Generally, triangle properties can be used to produce the features for image classification. To produce these features, triangle geometry need to be formed based on three coordinates which are the corners of A, B and C. However, not all triangle formations can be formed from the three coordinates due to the condition where corners of A, B and C may cause a straight line problem. The straight line problem occurs when the chosen coordinates of the corners of A, B and C are in a straight line which causes the triangle geometry impossible to be formed. On the other hand, the straight line occurs when the gradient of corners A, B and C produces the equivalent value. This can be proved by the experiment conducted to identify the gradient that has equivalent value where the position of coordinates A, B and C will determine either the triangle can be formed or vice versa. The purpose of this study is to suggest an improvement on triangle geometry shape through triangle point selection. To achieve this purpose, there are two objectives suggested for this study. They are: i) to propose straight line detection technique for corner A, B and C of triangle; and ii) to improve triangle shape by proposing location of corners based on dominant distribution of foreground image. In the experiment, four types of digit dataset are chosen which are IFCHDB, HODA, MNIST and BANGLA where each datasets is consisted of testing data and training data. The Detection of Triangle Point Selection (DTPS) is proposed to detect the triangle point that caused a straight line to be formed. Then, the straight line problem is solved using Triangle Geometry Based Dominant Distribution of Foreground Image (TD2FI). Next, the Triangle Features Based Summation of Gradient and Ratio (TSGR) and Enhancement of Proposed Triangle Features using Absolute Value (EFTA) are proposed in order to improve the classification accuracy result. The experimental results are yielded by comparing the results of classification accuracy between the present proposed methods with a prior proposed method using the supervised machine learning (SML). The SML used are the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP). The result of classification accuracy has shown impressive results for TD2FI, TSGR and EFTA methods through the SVM and MLP techniques whereas the datasets from IFCHDB, HODA and BANGLA respectively have acquired good results through the SVM technique while MNIST dataset has acquired the highest result of classification accuracy through the MLP technique. The result of classification accuracy for TD2FI is 94.723% from IFCHDB dataset, 97.295% from HODA dataset, 95.4% from MNIST dataset and 90.275% from BANGLA dataset. In conclusion, the proposed method is capable of outstripping the straight line issue based on the position of the coordinates of corners A, B and C as well as produce a better result for classification accuracy

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

State Of The Art In Digital Paleography

Digital paleography is an approach used to assist paleographers in deciding the origin of manuscripts. This is done by recording types of writings present in old manuscripts. It uses digital representation of book hands as a tool to support paleographical analyses by, human experts. There are six types of manuscripts selected which are Arabic, Chinese, Jawi, Indian, Latin and Roman. These types of manuscripts are discussed through their current contribution in the digital paleography field. The main purpose of this paper is to discuss the current work on digital paleography for selected types of manuscripts. Thus, we identified the approaches and methods used to define the types of handwritings in old manuscript

Offline Handwritten Digit Recognition Using Triangle Geometry Properties

Offline digit handwritten recognition is one of the frequent studies that is being explored nowadays.Most of the digit characters have their own handwriting nature. Recognizing their patterns and types is a challenging task to do.Lately,triangle geometry nature has been adapted to identify the pattern and type of digit handwriting.However,a huge size of generated triangle features and data has caused slow performances and longer processing time.Therefore,in this paper,we proposed an improvement on triangle features by combining the ratio and gradient features respectively in order to overcome the problem.There are four types of datasets used in the experiment which are IFCHDB,HODA,MNIST and BANGLA.In this experiment,the comparison was made based on the training time for each dataset Besides,Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) techniques are used to measure the accuracies for each of datasets in this study

Web-Based Dynamic Similarity Distance Tool

Similarity or distance measures is a well-known method and commonly used for calculating the distance between two samples of a dataset.Basically,the distance between the dataset samples is an important theory in multivariate analysis research.This paper proposes a tool that provides seven common distance methods that can be used in various research area.This tool is a web-based application which can be accessed through the internet browser.The objective of this tool is to introduce a web-based similarity distance application for many analysis and research purposes.Besides,a ranking method based on the Mean Average Precision is also implemented in this tool in order to increase the classification accuracies. This tool can process features that contain numerical values from any type of dataset

Frame Removal For Mushaf Al-Quran Using Irregular Binary Region

Segmentation is a process to remove frame or frame exists in each page of some releases of mushaf Al-Quran. The fault in segmentation process affects the holiness of Al-Quran. The difficulty to identify the appearance of frame around text areas as well as noisy black stripes has caused the segmentation process to be improperly carried out. In this paper, an algorithm for detecting the frame on Al-Quran page without affecting its content is proposed. Firstly, preprocessing was carried out by using the binarisation method. Then, it was followed with the process of detecting the frame in each page. In this stage, the proposed algorithm was applied by calculating the percentage of black pixel of binary from vertical (column) to horizontal (row). The results, based on experiments on several Al-Quran pages from different Al-Quran styles, demonstrate the effectiveness of the proposed techniqu

Framework Of Page Segmentation For Mushaf Al-Quran Based On Multiphase Level Segmentation

This paper presents the framework of page segmentation for Mushaf Al-Quran based on Multiphase Level Segmentation (MLS).This study focuses to (a) extract multiform frame shape by using a novel technique Neighbouring Pixel Behaviors (NPB) and (b) segment text line by using a novel technique which is Hybrid Projection Based Neighbouring Properties (HPBNP).Since Mushaf Al-Quran pages are decorated with a different type of pattern and design of a decorative frame.Thus,the decoration frame must be properly to extract out from a page of Mushaf Al-Quran first before properly get only the text of Mushaf Al-Quran regardless of its decoration heterogeneity.Therefore,NPB technique was proposed to remove multiform frame shape from the page of Mushaf Al-Quran.While the text of Mushaf Al-Quran has a several of diacritical marks,hence it will block the process of segmenting text line.Therefore,HPBNP technique was proposed for segment overlapping text line that interfered by diacritical marks or the stroke of the Arabic word. Experimental results of the proposed technique is shown in this paper

Multiform Frame Shape Extraction Using Neighbouring Pixel Be-Haviors For Mushaf Al-Quran

Page decoration removal in the Al-Quran is an important step in document processing. One of the challenging parts of Al-Quran text recognition area is text extraction from its decorated page. Al-Quran decorations contain different patterns and textures depending on the Mushaf. In this paper, we propose a novel approach based on neighbouring properties by analysing the binary image page of current neighbouring pixels of point. This paper will focus on how to extract Al-Quran text from its decorative illumination frame. The accuracy of the result of the proposed algorithm depends on the noise of the images. The proposed method may contribute to the development of applications related to Optical Character Recognition (OCR)