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Design And Implementation Of Gabor Filter Based Offline YorÙbà Handwritten Recognition System.
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CHAPTER TWO
LITERATURE REVIEW
2.1 PATTERN RECOGNITION
Pattern 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 pattern recognition, machine learning, data mining and knowledge discovery in databases (KDD) are hard to separate, as they largely overlap in their scope. Machine learning is the common term for supervised learning methods and originates from artificial intelligence, whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition has its origins in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition. In pattern recognition, there may be a higher interest to formalize, explain and visualize the pattern; whereas machine learning traditionally focuses on maximizing the recognition rates. Yet, all of these domains have evolved substantially from their roots in artificial intelligence, engineering and statistics; and have become increasingly similar by integrating developments and ideas from each other.( Wikipedia, 2014).
In machine learning, pattern recognition is the assignment of a label to a given input value. In statistics, discriminant analysis was introduced for this same purpose in 1936. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is "spam" or "non-spam"). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence labeling, which assigns a class to each member of a sequence of values (for example, part of speech tagging, which assigns a part of speech to each word in an input sentence); and parsing, which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence (Wikipedia, 2014).
Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output to the sort provided by pattern-recognition algorithms (Wikipedia, 2014).
Pattern recognition is studied in many fields, including psychology, psychiatry, ethology, cognitive science, traffic flow and computer science.
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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---
