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Design And Implementation Of Gabor Filter Based Offline YorÙbà Handwritten Recognition System.
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CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 SUMMARY
This 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 analysis of their implementation of stored handwriting images, this has greater value and less noise compared to the best known state of the art. This has confirmed to us in a clear term that Gabor Filter algorithm has a high quality in testing handwriting images.
Similarly, the study has been able to establish the fact that Gabor Filter algorithm performed better in recognition system by generating quality handwriting image.
5.2 CONCLUSION
In this project, the focus was on performance evaluation of handwriting image enhancement algorithms on Yoruba Recognition System. The handwriting image enhancement algorithms (Gabor Filter), which can adaptively improve the orientation of the structure and estimated frequency from inputted image. The algorithms were tested with different types of Yoruba handwriting images and the performance of these algorithms were evaluated based on Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).The result has shown that the quality of the generated images by Gabor filter algorithm has great value with less noise for the recognition of Yoruba words and characters.
5.3 RECOMMENDATIONS
To alleviate the stated problems on this study, the following recommendation were made:
1. For any application that required quality Yoruba recognition system, Gabor Filter algorithm should be adopted for image enhancement.
2. The Gabor Filter algorithm should be given a high impetus and priority over other enhancement algorithms.
3. The researchers in Handwriting Recognition System field should intensify a robust algorithm that can improve on the existing one.
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ABSRACT - [ Total Page(s): 1 ]ABSTRACT COMING SOON , CHECK OTHERS ... Continue reading---
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CHAPTER ONE - [ Total Page(s): 2 ]CHAPTER ONEINTRODUCTION1.1 BACKGROUND OF THE STUDYCharacter 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 chara ... 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---
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---
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