Development of an Improved Database for Yoruba Handwritten Character

Abstract

For improved human comprehension and autonomous machine perception, optical character recognition has been saddled with the task of translating printed or hand-written materials into digital text files. Many works have been proposed and implemented in the computerization of different human languages in the global community, but microscopic attempts have also been made to place Yoruba Handwritten Character on the board of Optical Character Recognition. This study developed a novel available dataset for research on offline Yoruba handwritten character recognition so as to fill the gaps in the existing knowledge. The developed database contains a total of 12,600 characters being made up of 70 classes from a total number of 200 writers, in which 80 % (10,500) is regarded as the training and validation dataset while the remaining 20 % (2,100) is regarded as testing dataset. The dataset is available on https://github.com/oluwashina90/Yoruba-handwritten-character-database. Hence, it is the complete and largest dataset available for Yoruba Handwritten character research.

A Hybridized Feature Extraction Model for Offline Yorùbá Document Recognition

“…Also, the Yorùbá handwriting character database can be designed in such a way that large amounts of data are available for testing. [32] presented a novel technique for developing a Yorùbá alphabet recognition system using deep learning. Although it is limited to Yorùbá characters, this study developed a deep convolutional network called YORBNET for detecting Yorùbá alphabets, taking into account all factors and metrics.…”