Grammar induction is one of the most important research areas of the natural language processing. The lack of a large Treebank, which is required in supervised grammar induction, in some natural languages such as Persian encouraged us to focus on unsupervised methods. We have found the Inside-Outside algorithm, introduced by Lari and Young, as a suitable platform to work on, and augmented IO with a history notion. The result is an improved unsupervised grammar induction method called History-based IO (HIO). Applying HIO to two very divergent natural languages (i.e., English and Persian) indicates that inducing more conditioned grammars improves the quality of the resultant grammar. Besides, our experiments on ATIS and WSJ show that HIO outperforms most current unsupervised grammar induction methods.