Character groundtruth for camera captured documents is crucial for training and evaluating advanced OCR algorithms. Manually generating character level groundtruth is a time consuming and costly process. This paper proposes a robust groundtruth generation method based on document retrieval and image registration for camera captured documents. We use an elastic non-rigid alignment method to fit the captured document image which relaxes the flat paper assumption made by conventional solutions. The proposed method allows building very large scale labeled camera captured documents dataset, without any human intervention. We construct a large labeled dataset consisting of 1 million camera captured Chinese character images. Evaluation of samples generated by our approach showed that 99.99% of the images were correctly labeled, even with different distortions specific to cameras such as blur, specularity and perspective distortion.
['Wei Fan', 'Koichi Kise', 'Masakazu Iwamura']
Automatic character labeling for camera captured document images