Abstract: Artificial Neural Networks (ANN `s) have been successfully applied to optical character recognition (OCR) yielding excellent results. This paper describes improvements to a system that recognize Arabic character in a low and high resolution binary document images. A classical conventional algorithm that uses chain coding for the segmentation of words, while an Learning Vector Quantization (LVQ) network is used to identify the segmented Arabic characters. Performance advances reflected in the current system largely result from the introduction of ensembles Freeman Arabic Classification Tree (FACT) (Al-Nasssiri, 2001), as the base for LVQ recognizer. By using features produced by chain coding algorithm, FACT, and LVQ (as a classifier), we have obtained high recognition rate on printed Arabic character. Application of LVQ demonstrates the arbitrary of the method to significantly reduce the computational lost of the classification system and improves the recognition rate. On characters extracted from more than 40 test images (pages) scanned with various kinds of scanners with 300 and 600 dpi scanning resolution, in addition to various degree of noise, the current system attains a character recognition rate within 88- 92%.