Detection of texts in natural images
This work addresses text detection in natural images, which is important for applications like document analysis and image retrieval, but it appears incremental as it builds on existing methods like connected components and SVMs.
The paper tackles the problem of detecting text in natural images by proposing a framework using connected components and a Gabor feature-based SVM, achieving a recall of 0.72 and precision of 0.88 on the ICDAR 10 dataset, which is better than benchmark methods.
A framework that makes use of Connected components and supervised Support machine to recognise texts is proposed. The image is preprocessed and and edge graph is calculated using a probabilistic framework to compensate for photometric noise. Connected components over the resultant image is calculated, which is bounded and then pruned using geometric constraints. Finally a Gabor Feature based SVM is used to classify the presence of text in the candidates. The proposed method was tested with ICDAR 10 dataset and few other images available on the internet. It resulted in a recall and precision metric of 0.72 and 0.88 comfortably better than the benchmark Eiphstein's algorithm. The proposed method recorded a 0.70 and 0.74 in natural images which is significantly better than current methods on natural images. The proposed method also scales almost linearly for high resolution, cluttered images.