Handwritten alphabet character recognition using audio signatures and machine learning
| dc.contributor.advisor | Ghaziasgar, Mehrdad | |
| dc.contributor.author | Beck, Bruce | |
| dc.date.accessioned | 2023-03-14T12:29:20Z | |
| dc.date.accessioned | 2024-10-30T14:00:35Z | |
| dc.date.available | 2023-03-14T12:29:20Z | |
| dc.date.available | 2024-10-30T14:00:35Z | |
| dc.date.issued | 2023 | |
| dc.description | >Magister Scientiae - MSc | en_US |
| dc.description.abstract | This research investigates the creation of an audio-based character recognition system that is able to segment, process and recognise uppercase English letters continuously drawn by the user on a given writing surface such as a table-top using a generic writing implement. The aim is to make use of the microphones on a single smartphone to capture the acoustic signal generated by the user as they draw letters on the writing surface, followed by the application of audio segmentation to subdivide the audio signal into segments corresponding to each letter, and finally the application of a combination of the Mel-Frequency Cepstral Coefficients feature descriptor and Support Vector Machines to recognise the segmented letters. | en_US |
| dc.identifier.uri | https://hdl.handle.net/10566/16901 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of the Western Cape | en_US |
| dc.rights.holder | University of the Western Cape | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Audio device | en_US |
| dc.subject | Computer security | en_US |
| dc.subject | Computer science | en_US |
| dc.title | Handwritten alphabet character recognition using audio signatures and machine learning | en_US |