In this paper, a natural scene character recognition method using convolutional neural network(CNN) and bimodal image enhancement is proposed. CNN based grayscale character recognizer has strong tolerance to degradations in natural scene images. Since character image is bimodal pattern image in essence, bimodal image enhancement is adopted to improve the performance of CNN classifier. Firstly, a maximum separability based color-to-gray method is used to strengthen the discriminative power in grayscale image space. Secondly, grayscale distribution normalization based on histogram alignment is performed. Through increasing the data consistency among grayscale training and test samples, it leads to a better CNN classifier. Thirdly, a shape holding grayscale character image normalization is adopted. Based on these measures, a high performance natural scene character recognizer is constructed. The recognition rate of 85.96% on ICDAR 2003 robust OCR dataset is higher than existing works, which verified the effectiveness of the proposed method.
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- Recognizing Natural Scene Characters by Convolutional Neural Network and Bimodal Image Enhancement
- Springer Berlin Heidelberg
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