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Design And Implementation Of Gabor Filter Based Offline YorÙbà Handwritten Recognition System.
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2.3 YORUBA ORTHOGRAPHY
Yoruba (English: /ˈjÉ’rÊŠbÉ™/; Yor. èdè Yorùbá) is a language spoken in West Africa. The number of speakers of Yoruba is approaching 30 million. It is a pluricentric language spoken principally in Benin and Nigeria, with communities in other parts of Africa, the Americas, and Europe. A variety of the language, Lucumi, is the liturgical language of the SanterÃa religion of the Caribbean. Many Yoruba words are used in the Afro-Brazilian religion known as Candomblé. Yoruba is also used in many other Afro-American religions in the Americas and the Caribbean. Yoruba is most closely related to the Itsekiri language (spoken in the Niger Delta) and to Igala (spoken in central Nigeria). (Wikipedia, 2018)
Yoruba is classified among the Edekiri languages, which together with Itsekiri and the isolate Igala form the Yoruboid group of languages within the Volta–Niger branch of the Niger–Congo family. The linguistic unity of the Niger–Congo family dates to deep prehistory, estimates ranging around 15,000 years ago (the end of the Upper Paleolithic). In present-day Nigeria, it is estimated that there are over 40 million Yoruba primary and secondary language speakers as well as several other millions of speakers outside Nigeria, making it the most widely spoken African language outside Africa. (Wikipedia, 2018)
The Yoruba group is assumed to have developed out of undifferentiated Volta–Niger populations by the 1st millennium BC. Settlements of early Yoruba speakers are assumed to correspond to those found in the wider Niger area from about the 4th century BC, especially at Ife. The North-West Yoruba dialects show more linguistic innovation than the Southeast and Central dialects. This, combined with the fact that the latter areas generally have older settlements, suggests a later date for migration into Northwestern Yorubaland.[7] According to the Kay Williamson Scale, the following is the degree of relationship between Itsekiri and other Yoruboid dialects, using a compiled word list of the most common words. A similarity of 100% would mean a total overlap of two dialects, while a similarity of 0 would mean two speech areas that have absolutely no relationship.
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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 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 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---
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