Unstructured to structured error correction using neural nets

Most transmitted or stored information are subjected to occasional errors. In most situations, the source of this information has inherent unstructured redundancy that can be exploited to correct these errors. In addition to the storage requirements, getting the source statistics required to perform the error correction may not be easy. In this paper, we propose and evaluate trained neural nets to transform the unstructured redundancy into a structured one. The new approach, eliminates the need for source statistics storage and also simplifies the decoding process. This idea is applied to correct some of the errors caused by passing a printed Arabic text through an optical character recognition (OCR) device. Simulation results demonstrate the effectiveness of this technique