A Hybrid Approach for Complex Layout Detection of Newspapers in Gurumukhi Script Using Deep Learning

Abstract

Layout analysis is the crucial stage in the recognition system of newspapers. A good layout analysis results in better recognition results. In this paper, we detected the complex layout of newspapers in the Gurumukhi script. We have used a hybrid approach. In this approach, firstly, we proposed an algorithm to remove pictures from newspaper images that involves various image preprocessing tasks based on binarization, finding contours, and erosion on the image to remove the graphics from the image. This method also removes pictures from complex non-Manhattan layouts. Finally, we have trained the deep-leaning model based on a convolutional network to detect the columns of text from newspapers. We have created a dataset of 500 images labelled with five classes on which the model was trained. We have tested this method on the number of newspapers of the Gurumukhi script. The results show very good accuracy with this hybrid approach of layout detection.