Bioinformatics and Handwriting/Speech Reconition: Uncoventional Applications of Similarity Search Tools
Year: 2006
Type: article
Abstract: This work introduces two unconventional applications for sequence alignment algorithms outside the domain of bioinformatics: handwriting recognition and speech recognition. In each application we treated data samples, such as the path of a and written pen stroke, as a protein sequence and use the FastA sequence alignment tool to classify unknown data samples, such as a written character. That is, we handle the handwriting and speech recognition problems like the protein annotation problem: given a sequence of unknown function, we annotate the sequence via sequence alignment. This approach achieves classification rates of 99.65% and 93.84% for the handwriting and speech recognition respectively. In addition, we provide a framework for applying sequence alignment to a variety of other non–traditional problems.
Authors: Kyle Jensen, Gregory Stephanopoulos
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2141267742