ICACT 2018
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/18944
Browse
2 results
Search Results
Item A Prototype P300 BCI Communicator for Sinhala Language(3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Manisha, U.K.D.N.; Liyanage, S.R.A Brain-Computer Interface (BCI) is a communication system which enables its users to send commands to a computer using only brain activities. These brain activities are generally measured by ElectroEncephaloGraphy (EEG), and processed by a system using machine learning algorithms to recognize the patterns in the EEG data. The P300-event related potential is an evoked response to an external stimulus that is observed in scalp-recorded electroencephalography (EEG). The P300 response has proven to be a reliable signal for controlling a BCI. P300 speller presents a selection of characters arranged in a matrix. The user focuses attention on one of the character cells of the matrix while each row and column of the matrix is intensified in a random sequence. The row and column intensifications that intersect at the attended cell represent the target stimuli. The rare presentation of the target stimuli in the random sequence of stimuli constitutes an Oddball Paradigm and will elicit a P300 response to the target stimuli. Emotive EPOC provides an affordable platform for BCI applications. In this study a speller application for Sinhala language characters was also developed for Emotiv users and tested. Classification of the P300 waveform was carried out using a dynamically weighted combination of classifiers. A mean letter classification accuracy of 84.53% and a mean P300 classification accuracy of 89.88% was achieved on a dataset collected from three users.Item Sinhala Character Recognition using Tesseract OCR(3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Manisha, U.K.D.N.; Liyanage, S.R.In Sri Lanka, there are many fields that uses Sinhala scripts, such as newspaper editors, writers, postal and application processes. In these fields there have only a scanned or printed copies of Sinhala script, where they have to enter them manually to a computerized system, which consumes much time and cost. The proposed method was consisted of two areas as image pre-processing and training the OCR classifier. In Image pre-processing, the scanned images were enhanced and binarized using image processing techniques such as grayscale conversion and binarization using local thresholding. In order to avoid distortions of scanned images such as water marks and highlights was removed through the grayscale conversion with color intensity changes. In the OCR training, the Tesseract OCR engine was used to create the Sinhala language data file and used the data file with a customized function to detect Sinhala characters in scanned documents. OCR engine was primarily used to create a language data file. First, pre-processed images were segmented (white letters in black background) using local adaptive thresholding where performing Otsu’s thresholding algorithm to separate the text from the background. Then page layout analysis was performed to identify non-text areas such as images, as well as multi-column text into columns. Then used detections of baselines and words by using blob analysis where each blob was sorted using the x-coordinate (left edge of the blob) as the sort key which enables to track the skew across the page. After the separation of each character, then labeled manually into Sinhala language characters. By using the Sinhala language data file into OCR function, it returns the recognized text, the recognition confidence, and the location of the text in the original image. By considering the recognition confidence of each word it is possible to control the accuracy of the system. The classifier was trained using 40 characters sets with 20 images from each character and tested using over 1000 characters (200 words) with variations of font sizes and achieved approximately 97% of accuracy. The elapsed time was less than 0.05 per a line with more than 20 words, which was a higher improvement than a manual data entering. Since the classifier can be retrained using testing images, it can be developed to achieve active learning.