GDL

Journal from gdlhub / 2020-04-07 11:53:34
Oleh : Leena Mary Francis, N. Sreenath, King Saud University
Dibuat : 2020-03-30, dengan 1 file

Keyword : Text detection, Support Vector Machine, Least Square Support Vector Machine, Machine Learning, Natural scene text extraction
Url : http://www.sciencedirect.com/science/article/pii/S131915781730126X
Sumber pengambilan dokumen : Web

Text detection from the natural scene is considered to be a challenging problem due to the complex background, varied light intensity at different locations, a large variety of colors, diverse font style and size. This paper focusses on detecting candidate text objects from the scene. The image is initially preprocessed to remove the noise and enhance the contrast. Then the various objects of the scene are marked and extracted forming a pool of objects. A set of candidate text objects are extracted from this pool of objects and given as output. In order to locate text candidates among these objects, we use Least-Square Support Vector Machine Technique, which trains the model using Char 74K character dataset and CIFAR 10 non-text image dataset. Finally, the trained model was applied to perform a binary classification of text and non-text objects. The results were evaluated over ICDAR 2015 scene images, MSRA500 and SVT datasets and also have been compared to other approaches acquiring encouraging results.

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