Handwriting removal method based on CNN

This paper proposes a handwriting removal method based on Convolutional Neural Network (CNN). In image preprocessing, to avoid the influence of lighting and color on paper document images, illumination equalization is realized based on Contrast Limited Adaptive Histogram Equalization (CLAHE). And then the OTSU threshold segmentation method is used for adaptive threshold segmentation to obtain a binary image. In terms of datasets production, word region segmentation is realized based on edge detection, morphological processing, and contour fitting to produce test set and training set images. which include 2800 handwriting and printing. Then it is applied to the training and testing of the CNN model, the classification accuracy reached 98.25 %. Finally, the handwriting region in paper document image is fitted and removed. The experimental results show that the algorithm can better remove the handwriting content in paper document image, it is helpful to standardize the processing of paper documents.