Name
Abstract | ||
|---|---|---|
Text detection and recognition from images have numerous applications for document analysis and information retrieval tasks. An accurate and robust method for detecting texts in natural scene images is proposed in this paper. Text-region candidates are detected using maximally stable extremal regions (MSER) and a machine learning based method is then applied to refine and validate the initial detection. The effectiveness of features based on aspect ratio, GLSM, LBP, HOG descriptors are investigated. Text-region classifiers of MLP, SVM and RF are trained using selections of these features and their combination. A publicly available multilingual dataset ICDAR 2003,2011 has been used to evaluate the method. The proposed method achieved excellent performance on both databases and the improvements are significant in terms of Precision, Recall, and F-measure. The results show that using a suitable feature combination and selection approach can can significantly increase the accuracy of the algorithms. Keywords-text detection; scene images; ICDAR; feature selection |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3109761.3109803 | PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND MACHINE LEARNING (IML'17) |
Keywords | Field | DocType |
text detection, scene images, ICDAR, feature selection | Document analysis,Pattern recognition,Feature selection,Computer science,Support vector machine,Computer network,Feature combination,Maximally stable extremal regions,Artificial intelligence,Recall,Text detection | Conference |
Citations | PageRank | References |
0 | 0.34 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanaa F. Mahmood | 1 | 0 | 0.34 |
| Baihua Li | 2 | 176 | 21.71 |
| E. A. Edirisinghe | 3 | 99 | 20.09 |