Name
Abstract | ||
|---|---|---|
This paper presents an algorithm for reading digital video clocks reliably and quickly. Reading digital clocks from videos is difficult due to the challenges such as color variety, font diversity, noise, and low resolution. The proposed algorithm overcomes these challenges by using the novel methods derived from the domain knowledge. This algorithm first localizes the digits of a digital video clock and then recognizes the digits representing the time of digital video clock. It is a robust three-step algorithm. The first step is an efficient procedure that directly identifies the region of the second digit at a very low computational cost, which replaces the traditional tedious image processing procedure of identifying the second digit region. The success of the first step mainly leverages on the novel second-pixel periodicity method. Using the acquired second digit region as input, the second step is a clock digit localization procedure. It first acquires the colors of the digits of the digital video clock and performs the color conversion. Then it localizes the remaining clock digits. Finally, the last step is a clock digit recognition procedure. It first employs an enhanced digit-sequence recognition method to robustly recognize the digits on the second; it then adopts a deep learning procedure to recognize the remaining digits. The proposed algorithm is tested on a prepared benchmark of 1000 videos that is publicly available and the experimental results show that it can read digital video clocks with a 100% accuracy at a low computational cost. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1142/S021800141555006X | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Digit localization, digit recognition, digital video clock, second-pixel periodicity, deep learning, conditional random field | Conditional random field,Digital video,Computer vision,Domain knowledge,Computer science,Font,Numerical digit,Image processing,Speech recognition,Artificial intelligence,Deep learning,Digit recognition | Journal |
Volume | Issue | ISSN |
29 | 4 | 0218-0014 |
Citations | PageRank | References |
6 | 0.47 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinguo Yu | 1 | 443 | 40.77 |
| Wan Ding | 2 | 24 | 5.29 |
| Zhizhong Zeng | 3 | 9 | 1.19 |
| Hon-Wai Leong | 4 | 475 | 44.84 |