Demo Data from VLDB 2026 / reviewers in the wild / expert
Mike Usher
dblp:99/3496
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1990
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Image recognition and object detection · 100% | |
| Computer graphics and multimedia
1 paper |
Image and video processing · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Image recognition and object detection
handwriting recognition |
0.0 | 1 | 1990 | On-Line Recognition of Handwritten Arabic Characters · IEEE Trans. Pattern Anal. Mach. Intell. 1990 |
Image and video processing › document image analysis
character segmentation |
0.0 | 1 | 1990 | On-Line Recognition of Handwritten Arabic Characters · IEEE Trans. Pattern Anal. Mach. Intell. 1990 |
Methods — techniques the papers use, named apart from their topics
stroke feature extraction · 0.0rule-based matching · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1990 | On-Line Recognition of Handwritten Arabic CharactersabstractArabic characters are always in cursive script. Handwritten words were entered into an IBM PC via a graphics tablet and a segmentation process applied to the points; the length and the slope of each segment was then found, and the slope categorized into one of four directions. In the learning process, specifications on the strokes of each character are fed to the computer. In the recognition process, the parameters of each stroke are found and special rules applied to select the collection of strokes which best matches the features of one of the stored characters. The results are promising, and suggestions for improvements leading to 100% recognition are proposed.> Samir Al-Emami, Mike Usher |
IEEE Trans. Pattern Anal. Mach. Intell. | 2 |