A deep learning approach to handwritten text recognition in the presence of struck-out text

The accuracy of handwritten text recognition may be affected by the presence of struck-out text in the handwritten manuscript. This paper investigates and improves the performance of a widely used handwritten text recognition approach Convolutional Recurrent Neural Network (CRNN) on handwritten lines containing struck out words. For this purpose, some common types of struck-out strokes were superimposed on words in a text line. A model, trained on the IAM line database was tested on lines containing struck-out words. The Character Error Rate (CER) increased from 0.09 to 0.11. This model was re-trained on dataset containing struck-out text. The model performed well in terms of struck-out text detection. We found that after providing an adequate number of training examples, the model can deal with learning struck-out patterns in a way that does not affect the overall recognition accuracy.