Handwritten　Chinese　text　recognition　characters　is　a　challenging　problem　as　it　involves　a　imbalanced　training　data,　and　the　samples　are　very　different　even　in　same　character.　In　this　paper,　we　propose　a　novel　algorithm　based　on　the　bidirectional　Recurrent　Neural　Network　(BiRNN)　to　recognize　the　characters　in　the　text　regions.　We　solve　the　problems　with　pre-processing　and　improved　CNN　network.　In　addition,　we　utilize　RNN　to　analyze　the　correlation　between　characters.　Compared　with　previous　works,　the　algorithm　has　three　distinctive　properties:　(1)　It　can　predict　characters　by　context　analyzing　from　forward　and　backward.　(2)　It　solve　the　problem　of　sample　imbalance　effectively.　(3)　The　convergence　rate　of　training　has　increased.　Moreover,　the　proposed　algorithm　has　achieved　good　results　in　recognition.　©　2019,　Springer　Nature　Switzerland　AG.