-
Design And Implementation Of Gabor Filter Based Offline YorÙbà Handwritten Recognition System.
CHAPTER ONE -- [Total Page(s) 2]
Page 1 of 2
-
-
-
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
Character is the basic building block of any language which is used to develop different language structures. Characters are alphabets and the structures developed are the words, strings, sentences, paragraphs and so on (Le Cun et al., 1990). Character recognition also known as optical character recognition is the recognition of optically processed characters. The purpose of character recognition is to interpret input as a sequence of characters from an already existing set of characters (Kader and Deb, 2012).
Handwritten character recognition is the process of converting handwritten text into a form that can be read by the computer, the major problem in handwritten character recognition system is the variation of the handwriting styles of individuals, which can be completely different for different writers (Patel and Thakkar, 2015).
Handwritten character recognition system can be divided into two categories namely the online character recognition and the offline character recognition.
Online character recognition is the conversion of text written on a digitizer or PDA automatically where the sensor picks up the pen - tip movements and the pen-up/pen-down switching. The signal obtained from the pen - tip movements is converted into letter codes that can be used by the system and text processing applications. In offline character recognition, the image of the written text is scanned and sensed offline by optical scanning (optical character recognition) or intelligent character recognition (Tawde and Kundargi, 2013).
Yorùbá (natively èdè Yorùbá) is a Niger–Congo language spoken in West Africa. The number of speakers of Yoruba was estimated at around 20 million in the 1990s. The native tongue of the Yoruba people is spoken principally in Nigeria and Benin, with communities in other parts of Africa, Europe and the Americas. A variety of the language, Lucumi, is the liturgical language of religion of the Caribbean. Yoruba the SanterÃa is most closely related to the Owo and Itsekiri languages (spoken in the Niger Delta) and to Igala (spoken in central Nigeria).
There has been extensive work in the literature regarding features extraction approaches in the off-line Arabic handwriting recognition. Many of these methods require high quality binarization of the document images which is difficult due to varying characteristics of noisy artifacts common in such documents. In addition, large amount of gray-level information is lost during binarization. Therefore, features that are extracted from the original gray-level images should be useful to discriminate handwritten character shapes (Jin Chen et al, 2017).
Gabor ï¬lters, which operate directly on gray-level images, have several advantages. First, Gabor features have been used for capturing local information in both spatial and frequency domains from images, as opposed to other global techniques such as Fourier Transforms. Second, Gabor ï¬lters are orientation speciï¬c. This property allows us to analyze stroke directions in the handwriting. Third, the ï¬ltering output is robust to various noises since Gabor ï¬lters use information from all pixels in the kernel (Jin C. et al, 2017).This research works tends to use Gabor features extraction techniques on Yoruba characters.
1.2 STATEMENT OF PROBLEM
Many feature extraction approaches for offline handwriting recognition (OHR) rely on accurate binarization of gray level Images, However, high-quality binarization of most real-world documents is extremely difficult due to varying characteristics of noises artifacts common in such documents, hence the research work used consider Gabor features for off-line Yorùbá handwritten images.
1.3 AIM AND OBJECTIVES
The aim of the project is to design and implementation of Gabor Filter based offline Yorùbá handwritten recognition system.
The objectives of this project are to:
1. Review the existing literature on Yoruba handwriting recognition system
2. Design a Gabor filter based Yoruba Handwriting Recognition system Implementing the designed system in “2â€.
CHAPTER ONE -- [Total Page(s) 2]
Page 1 of 2
-
-
ABSRACT - [ Total Page(s): 1 ]ABSTRACT COMING SOON , CHECK OTHERS ... Continue reading---
APPENDIX A - [ Total Page(s): 11 ]s=s+n; e.putString("d"+num,s); e.commit(); new AlertDialog.Builder(MainActivity.this) .setMessage(R.string.learn_sample) .setNeutralButton(R.string.ok,null) .show(); dv.resetPath(); Paths.reset(); dv.invalidate(); } } ... Continue reading---
CHAPTER TWO - [ Total Page(s): 7 ]CHAPTER TWO LITERATURE REVIEW2.1 PATTERN RECOGNITIONPattern recognition is nearly synonymous with machine learning. This branch of artificial intelligence focuses on the recognition of patterns and regularities in data. In many cases, these patterns are learned from labeled "training" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).The terms ... Continue reading---
CHAPTER THREE - [ Total Page(s): 3 ]CHAPTER THREERESEARCH METHODOLOGY3.1 DATA ACQUISTIONThe Yoruba handwriting images used in this project are those were created for the purpose of this project. This database was only recently assembled by the author of this project, and before this there was no standard database for this field. The database consists of a collection of Yoruba characters images, each containing one character. The images come from Ten (10) different writers, mostly students. All the ï¬gures in this th ... Continue reading---
CHAPTER FOUR - [ Total Page(s): 4 ]CHAPTER FOURRESULT AND DISCUSSIONS4.1 SYSTEM RESULT ANALYSISBased on the definition given in Handwriting recognition system, 50% of the respondents can be classified as Strong accurate writers, 30% as accurate writers, 15% as Non poor writers and 5% as poor writers. This shows that 95% of handwriting image in the project belong to strong accurate and accurate writers.As far as the gender is concerned, 60% of the respondents were male and 40% were female. This indicates that men are more ap ... Continue reading---
CHAPTER FIVE - [ Total Page(s): 1 ]CHAPTER FIVESUMMARY, CONCLUSION AND RECOMMENDATION5.1 SUMMARYThis project is predicated by the need and necessity to examine the performance evaluation of the Yoruba handwriting image enhancement algorithms. In a bid to achieve this, the Gabor Filter algorithm was used in order to enhance handwriting images so as to test the quality and efficiency recognition.Having implemented this, the levels of performance of the handwriting image enhancement algorithms (Gabor Filter) by comparing the a ... Continue reading---
REFRENCES - [ Total Page(s): 1 ]REFERENCEHuang, B.; Zhang, Y. and Kechadi, M.; Preprocessing Techniques for Online Handwriting Recognition. Intelligent Text Categorization and Clustering, Vol. 164, 2009.J.Pradeep, E.Srinivasanand S.Himavathi, Diagonal based feature extraction for handwritten alphabets recognition System using neural network, Vol 3, No 1, Feb 2011.Jin Chen, Huaigu Cao, Rohit Prasad, Anurag Bhardwaj and Prem Natarajan,Gabor Features for Offline Arabic Handwriting Recognition, 10, June 9-11, 2010.Jumoke F. A ... Continue reading---
