Deep-CNNTL: Text Localization from Natural Scene Images Using Deep Convolution Neural Network with Transfer Learning

Text localization from natural images plays an essential role in reading the text content present in the illustration. It is complex to localize the textual content because the text in natural scene images will be scattered. Prior information about the location of the text, size of the text, the orientation of the text, and the number of text present in the images are not available. These factors have posed a challenge to localize text in natural scene images. We have proposed a comprehensive solution for localizing text using Deep Convolution Neural Network (DCNN) and Transfer Learning (TL). DCNN layers such as convolution, dense layers, dropout, and learning rate are optimized using a random search. A combination of DCNN+TL is more effective in processing complex text images using VGG16 architecture. The proposed method has experimented on the standard ICDAR 2015 dataset, and the obtained results proved to be more effective with accuracy and an F-score of 0.8279 compared to state-of-art methods.