Abstract
Robust Recognition of Chinese Text from Cellphone-acquired Low-quality Identity Card Images Using Convolutional Recurrent Neural Network
Highlights
Identity (ID) cards are a kind of legal certificate to prove the residential ID of the holder in China and are widely used in all aspects of modern social life
2000 real ID card images taken by cellphone camera are provided by a construction company under a privacy agreement that prohibited us from revealing the full information of any individual
Where lrepresents the predicted label sequence and l represents the ground-truth label sequence; [2] line recognition accuracy (LRA), i.e., the percentage of text line images correctly recognized, where the text line image is correctly recognized if no character is misidentified
Summary
Identity (ID) cards are a kind of legal certificate to prove the residential ID of the holder in China and are widely used in all aspects of modern social life. To read customer information automatically from uploaded ID card images, optical character recognition (OCR) technology is needed. Synthetic datasets provide detailed ground-truth annotations, which are cheap and scalable alternatives to annotating images manually They have been widely used to learn scene text recognition models[4,5] and scene text detection models.[6] The second contribution of this paper is that a novel ID card text image generator (G) based on a conditional generative adversarial network (cGAN) named pix2pix[7] is proposed, which is capable of emulating ID card text images in a natural environment in the case of a small number of real ID card images
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