A Robust Approach to Digit Recognition in Noisy Environments

The article presents an original approach to optical character recognition (OCR) used in real environments, such as gas- and electricity-meters, where the quantity of noise is sometimes as large as the quantity of good signal. This approach uses two algorithms for better results. These are a neural network on one hand, respectively the k-nearest neighbor as the confirmation algorithm. Unlike other OCR systems, this one is based on the angles of the digits, rather than on pixels. This makes it insensitive to the possible rotations of the digits, respectively to the quantity of noise that may appear in an image. We will prove that the approach has several advantages, such as: insensitivity to the possible rotations of the digits, the possibility to work in different light and exposure conditions, the ability to deduct and use heuristics for character recognition.

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