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

Digging Deeper into CRNN Model in Chinese Text Images Recognition