Naser, E. (2020). Word Retrieval based on FREAK Descriptor to Identify the Image of the English Letter that Corresponds to the First Letter of the Word. Adab Al-Kufa, 38(3B), 150-160. doi: 10.30684/etj.v38i3B.1511
Ekhlas F. Naser. "Word Retrieval based on FREAK Descriptor to Identify the Image of the English Letter that Corresponds to the First Letter of the Word". Adab Al-Kufa, 38, 3B, 2020, 150-160. doi: 10.30684/etj.v38i3B.1511
Naser, E. (2020). 'Word Retrieval based on FREAK Descriptor to Identify the Image of the English Letter that Corresponds to the First Letter of the Word', Adab Al-Kufa, 38(3B), pp. 150-160. doi: 10.30684/etj.v38i3B.1511
Naser, E. Word Retrieval based on FREAK Descriptor to Identify the Image of the English Letter that Corresponds to the First Letter of the Word. Adab Al-Kufa, 2020; 38(3B): 150-160. doi: 10.30684/etj.v38i3B.1511

Word Retrieval based on FREAK Descriptor to Identify the Image of the English Letter that Corresponds to the First Letter of the Word

Engineering and Technology Journal
Article 7, Volume 38, 3B, Autumn 2020, Page 150-160  XML PDF (608 K)
Document Type: Research Paper
DOI: 10.30684/etj.v38i3B.1511
Author
Ekhlas F. Naser
Department of Computer Science, University Of Technology, Baghdad, Iraq
Abstract
For the reason of colossal technological developments, the requirement of image information methods became a significant issue. The aim of this research was to retrieve the word based on Fast Retina Key-points (FREAK) descriptor .The suggested system consists of four stages. In the first stage, the images of English letters are loaded. Points are detected via SUSAN in the second stage. FREAK used in the third stage and then a database was created containing 26 English letters. The image to be tested was entered and the points are extracted in the fourth stage and then Manhattan distance was used to calculate the distance between the value of the test image descriptors and all the values of the descriptors in a database. The experimental results show that the precision and the recall values were high for retrieval of the words when using SUSAN because it extracts a large number of interest points compared to the Harris method. For example, for the letter H was 104 with SUSAN while it was 42 for Harris, therefore; the precision for retrieval of the word Hour was 89% and recall was 93% when using SUSAN while precision was 77% and recall was 80% when using Harris.
Keywords
FREAK; SUSAN; Harris; image retrieval; Manhattan distance
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