License plate recognition method is an important part of modern intelligent traffic management system and has been widely used in many fields. However， in practical application scenarios， there are multi-row license plates， and the traditional methods are not flexible enough to deal with multi-row license plates， and cannot achieve high-precision end-to-end recognition. Therefore， an end-to-end recognition method based on 2D Space Traversal Long Short-Term Memory （2DST-LSTM） network was proposed to recognize single-row and double-row license plates. The proposed method abandons the previous image segmentation step， and carries out license plate recognition in an end-to-end way， which makes the license plate recognition more efficient and accurate. 2DST-LSTM can improve the recognition effect of license plates， especially double-row license plates， in complex environment. Experimental results on multiple datasets show that the proposed method achieves the highest recognition rate up to 98.6% for double-row license plates， which verified its effectiveness.