We present two dependency parsers for Persian, MaltParser and MSTParser, trained on theUppsala PErsian Dependency Treebank. The treebank consists of 1,000 sentences today. Itsannotation scheme is based on Stanford Typed Dependencies (STD) extended for Persianwith regard to object marking and light verb contructions. The parsers and the treebank aredeveloped simultanously in a bootstrapping scenario. We evaluate the parsers by experimentingwith different feature settings. Parser accuracy is also evaluated on automatically generated andgold standard morphological features. Best parser performance is obtained when MaltParseris trained and optimized on 18,000 tokens, achieving 68.68% labeled and 74.81% unlabeledattachment scores, compared to 63.60% and 71.08% for labeled and unlabeled attachmentscore respectively by optimizing MSTParser.