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Language, the most apparent distinction between the human and the animal, has been focused by the data scientists in recent decades. They are dying to discover a method that the human terms can be translated into machine language. After the seething of the English recognition, more researchers focus on the Chinese recognition both in China and abroad. However, differing with the English recognition, Chinese recognition is more challenging because of the tremendous character categories. Moreover, compared with single character, Chinese text recognition can be more challenging due to the join-up and the different writing styles. In this article, three algorithms about the Chinese text recognition are going to be illustrated and evaluated, including the segmentation recognition and the no segmentation. In the segmentation recognition part, we will describe briefly about the single character recognition because the segmentation recognition process includes this representative method, and we also introduce a classical algorithm about Chinese text recognition written by Wang in 2012 [1]. In the no segmentation part, we display two kinds of ways to solve the problem. One is based on HMM and Another is based on FCRN.
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