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Abstract:Optical character recognition (OCR) automatically recognizes text in an image. OCR is still a challenging problem in computer vision. A successful solution to OCR has important device applications, such as text-to-speech conversion and automatic document classification. In this work, we analyze character recognition performance using the current state-of-the-art deep-learning structures. One is the AlexNet structure, another is the LeNet structure, and the other one is the SPNet structure. For this, we have built our own dataset that contains digits and upper-and lowercase characters. We experiment in the presence of salt-and-pepper noise or Gaussian noise, and report the performance comparison in terms of recognition error. Experimental results indicate by five-fold cross-validation that the SPNet structure (our approach) outperforms AlexNet and LeNet in recognition error.

In this paper we propose an effective approach for creating nice-looking photo images of scenes having high dynamic range using a set of photos captured with exposure bracketing. Usually details of dark parts of the scene are preserved in over-exposed shot, and details of brightly illuminated parts are visible in under-exposed photos. A proposed method allows preservation of those details by first constructing gradient field, mapping it with special function and then integrating it to restore lightness values using Poisson equation. Resulting image can be printed or displayed on conventional displays.

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