Ancient Horoscopic Palm Leaf Binarization Using A Deep Binarization Model - RESNET

Abstract

Binarization of ancient documents is a challenging task. Nowadays lot of traditional binarization algorithms exist with good accuracy but those algorithms cannot remove all kind of noises which are present in the same ancient documents. In traditional RESNET batch normalization is not using because of that it takes too much time for training. But proposed RESNET uses batch normalization which will increase the speed of the model training. Also, it is true huge data set can’t be used at same time for enhancement. So, the deep learning models like RESNET will remove noise from ancient documents with good accuracy. The modified RESNET model will give good accuracy in ancient degraded image enhancement. Residual network will remove the noises like ink bleed and uneven illumination. In modified RESNET model with batch normalization which will increase the speed of the training phase. Proposed work is mainly based on modified RESNET with Convolution and Batch normalization along with Relu as one block like which five blocks are used for image binarization. It is working based on two phase method like down-sampling and up-sampling which is used to efficiently binarize the degraded ancient palm leaf manuscript with an accuracy of 95.38%.