Live detection of text in the natural environment using Convolutional Neural Network

Abstract

Abstract With the exponential growth in the quantity of born-digital images, the problem of comprehending text from natural scene images has acquired greater significance. This paper proposes a deep-learning based approach, to detect the presence of text in the natural scene images. The proposed approach is built with the capability to distinguish text and non-text images from the live-stream of the smart-phone camera, thereby eliminating the need for capturing the image, to locate the presence of text. A streamlined Convolutional Neural Network (CNN) MobileNet is harnessed for the process of distinguishing text images from non-text images. The proposed approach can be adopted as a filter, to decide whether to permit the image further down the processing pipeline for the text detection task, which in turn leads to the reduction in false-positives and false-negatives by not processing an image which does not have text. It was inferred from the experimental results that the width multiplier value of 0.75 and resolution multiplier of 224 yields the accuracy of 99.31% in classifying the text images from non-text images.