Design And Implementation Of Gabor Filter Based Offline YorÙbÁ Handwritten Recognition System.

The result of the wordlist analysis shows that Itsekiri bears the strongest similarity to the SEY dialects and most especially Ilaje and Ikale, at 80.4% and 82.3% similarity. According to the language assessment criteria of the international Language Assessment Conference (1992), only when a wordlist analysis shows a lexical similarity of below 70% are two speech forms considered to be different languages. An overlap of 70% and above indicates that both speech forms are the same language, although dialect intelligibility tests would need to be carried out to determine how well speakers of one dialect can understand the other speech form. Thus while the
analysis shows that Igala, with an overlap of 60% is a completely different language, all other Yoruboid speech forms are merely dialects of the same Language.
2.4      HANDWRITING RECOGNITION
Handwriting recognition refers to the identification of written characters. The problem can be viewed as a classification problem where we need to identify the most appropriate character the given figure matches to. Offline character recognition refers to the recognition technique where the final figure is given to us. (Rahul KALA et al, 2010)
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. 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. (Oladele M.O et al, 2017)
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. Handwritten character recognition system can be divided into two categories namely the online character recognition and the offline character recognition. (Oladele M.O et al, 2017)
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. (Oladele M.O et al, 2017)
2.5    RELATED WORKS
There are several research works on handwritten character recognition but there are few ones on Yorùbá character recognition. Few of the existing works on handwritten character recognition are highlighted in this sub-section.
(Oladele, M.O. Adepoju, T.M. Omidiora, E.O. Sobowale, A.A.Olatoke, O.A Ayeleso, E.C. (2017)
Offline Yorùbá Handwritten Character Recognition
A system for recognizing Yorùbá handwritten characters was presented. The system was developed using the SVM classifier. Both the training and testing were done using SVM. The performance was evaluated based on recognition rate and rejection rate. The result shows that the recognition rate of the system was 76.7% while the rejection rate was 23.3%.
Mohamed Elleuch, Ansar Hani and Monji Kherallah (2017)