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Design And Implementation Of Gabor Filter Based Offline YorÙbà Handwritten Recognition System.
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1.4 PROPOSED RESEARCH METHODOLOGY
1. Gathering of books and online PDF books on related work of handwriting recognition system.
2. Designing the system using Object Oriented Approach.
3. Implementing the designed system using C++ programming Language
1.5 JUSTIFICATION
The Yoruba handwriting recognition system is one of most important software in university for both students and instructors because of the development in technology.
1.6 SCOPE OF THE STUDY
This research work covers the performance of sub word recognition for off-line Yorùbá handwritten images. We will also compare the recognition performance with other binarization based features which have been proven to be effective in capturing shape characteristics of handwritten Arabic sub words, such as GSC (a set of gradient, structure, and concavity features) and skeleton based Graph features.
1.7 CHAPTER LAYOUT
The chapter one consists of the introduction that is background of the study, statement of the problem, aim and objective of the study, proposed methodology, research justification and finally the chapter layout. Chapter two consists of review of related concept and related works. Chapter three consists of research methodology. Chapter four consists of result discussion and finally, chapter five consists of summary, conclusion and recommendation.
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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---
