The translation quality of Statistical Machine Translation (SMT) depends on the amount of input data especially for morphologically rich languages. Farsi (Persian) language is such a language which has few NLP resources. It also suffers from the non-standard written characters which causes a large variety in the written form of each character. Moreover, the structural difference between Farsi and English results in long range reorderings which cannot be modeled by common SMT reordering models. Here, we try to improve the existing English-Farsi SMT system focusing on these challenges first by expanding our bilingual limited-domain corpus to an open-domain one. Then, to alleviate the character variations, a new text normalization algorithm is offered. Finally, some hand-crafted rules are applied to reduce the structural differences. Using the new corpus, the experimental results showed 8.82% BLEU improvement by applying new normalization method and 9.1% BLEU when rules are used.