  Automatic text image recognition is a prevalent application in computer
vision field. One efficient way is use Convolutional Recurrent Neural
Network(CRNN) to accomplish task in an end-to-end(End2End) fashion. However,
CRNN notoriously fails to detect multi-row images and excel-like images. In
this paper, we present one alternative to first recognize single-row images,
then extend the same architecture to recognize multi-row images with proposed
multiple methods. To recognize excel-like images containing box lines, we
propose Line-Deep Denoising Convolutional AutoEncoder(Line-DDeCAE) to recover
box lines. Finally, we present one Knowledge Distillation(KD) method to
compress original CRNN model without loss of generality. To carry out
experiments, we first generate artificial samples from one Chinese novel book,
then conduct various experiments to verify our methods.
