A deep learning approach to handwritten text recognition in the presence of struckout text The accuracy of handwritten text recognition may be affected by the presence of struckout 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 struckout strokes were superimposed on words in a text line. A model, trained on the IAM line database was tested on lines containing struckout words. The Character Error Rate (CER) increased from 0.09 to 0.11. This model was retrained on dataset containing struckout text. The model performed well in terms of struckout text detection. We found that after providing an adequate number of training examples, the model can deal with learning struckout patterns in a way that does not affect the overall recognition accuracy.

Efficient, LexiconFree OCR using Deep Learning Contrary to popular belief, Optical Character Recognition (OCR) remains a challenging problem when text occurs in unconstrained environments, like natural scenes, due to geometrical distortions, complex backgrounds, and diverse fonts. In this paper, we present a segmentationfree OCR system that combines deep learning methods, synthetic training data generation, and data augmentation techniques. We render synthetic training data using large text corpora and over 2000 fonts. To simulate text occurring in complex natural scenes, we augment extracted samples with geometric distortions and with a proposed data augmentation technique  alphacompositing with background textures. Our models employ a convolutional neural network encoder to extract features from text images. Inspired by the recent progress in neural machine translation and language modeling, we examine the capabilities of both recurrent and convolutional neural networks in modeling the interactions between input elements. The proposed OCR system surpasses the accuracy of leading commercial and opensource engines on distorted text samples.