The Method of Clearing Printed and Handwritten Texts from Noise

  • S. ChernenkoEmail author
  • S. Lychko
  • M. Kovalkova
  • Y. Esina
  • V. Timofeev
  • K. Varshamov
  • A. Karlov
  • A. Pozdeev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1156)


The article reviews the existing methods and algorithms for clearing printed and handwritten texts from noise and proposes an alternative approach. Among the solutions analyzed, a group of methods based on adaptive threshold conversion is distinguished. Our method for clearing print and handwritten documents from noise is based on using of a convolutional neural network ensemble with a U-Net architecture and a multi-layer perceptron. Using consequently a convolutional neural network and a multilayer perceptron demonstrates high efficiency in small training sets. As a result of applying our method to the entire test sample, an image cleaning degree of 93% was achieved. In the future, these methods can be introduced in libraries, hospitals, news companies where people work with non-digitized papers and digitization is needed.


Computer vision Artificial intelligence CNN Neural networks Segmentation OCR U-net Backpropagation 


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