A light-weight natural scene text detection and recognition system

Scene text recognition is an application of Computer Vision that analyses the scene image and recognizes the text present on it. This task has many applications and will gain more importance if it can be used in handheld devices. The problem with existing methods is that if the model has a huge number of parameters and complex architectures, then the model will have a huge file size which will be problematic to deploy the application on mobile devices. Therefore, the aim of this paper is to propose a light-weight model that is a model with less number of parameters, small file size and less complexity that can be used in platforms with limited resources while achieving a comparable accuracy with those of the heavy weight models. The proposed models rely on deep learning to handle most of the steps automatically, consume less time and give precise results after facing many challenges. The proposed scene text recognition model is in the form of a Convolutional-Recurrent Neural network where the Convolution network extracts the features from the cropped images of scene text and the Recurrent network processes the sequential data of varying length present in the cropped images. After training, the scene text recognition model generates a weight file of 12 MB with 1 M parameters. To reduce number of parameters, weight of files and to show trade-off between efficiency and accuracy, MobileNetV2 is used in place of Convolution network that generates weight file of 6 MB with 0.5 M parameters. The performance on ICDAR 2013, IIIT 5K and Total-Text datasets shows that the proposed work performs well in detecting and recognizing texts from natural scene images.