Name
Abstract | ||
|---|---|---|
AbstractText detection in natural scene images is an interesting problem in the field of information retrieval. Several methods have been proposed over the past few decades for scene text detection. However, the robustness and efficiency of these methods are downgraded due to high sensitivity towards various complexities of an image. Also, in multi-lingual environment where texts may occur in multiple languages, a method may not be suitable for detecting scene texts in certain languages. To counter these challenges, a gradient morphology-based method is proposed in this paper that proves to be robust against image complexities and efficiently detects scene texts irrespective of their languages. The method is validated using low quality images from standard multi-lingual datasets like MSRA-TD500 and MLe2e. The performance of the method is compared with that of some state-of-the-art methods, and comparably better results are observed. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.4018/IJCVIP.2020070103 | Periodicals |
DocType | Volume | Issue |
Journal | 10 | 3 |
ISSN | Citations | PageRank |
2155-6997 | 1 | 0.35 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dibyajyoti Dhar | 1 | 1 | 0.35 |
| Neelotpal Chakraborty | 2 | 6 | 2.12 |
| Sayan Choudhury | 3 | 1 | 0.35 |
| Ashis Paul | 4 | 1 | 0.35 |
| Ayatullah Faruk Mollah | 5 | 33 | 8.59 |
| Subhadip Basu | 6 | 385 | 43.75 |
| Ram Sarkar | 7 | 420 | 68.85 |