Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks

1 Apr 2018Andreas KölschAshutosh MishraSaurabh VarshneyaMuhammad Zeshan AfzalMarcus Liwicki

This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents. The handwritten annotations can appear in form of underlines and text by using various writing instruments, e.g., the use of pencils makes the data more challenging... (read more)

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