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We studied continuous sign language recognition using Doppler radar sensors. Four signs in Chinese sign language and American sign language were captured and extracted by complex empirical mode decomposition (CEMD) to obtain spectrograms. Image sharpening was used to enhance the micro-Doppler signatures of the signs. To classify the different signs, we utilized an improved Yolov3-tiny network by replacing the framework with ResNet and fine-tuned the network in advance. This method can remove the epentheses from the training process. Experimental results revealed that the proposed method can surpass the state-of-the-art sign language recognition methods in continuous sign recognition with a precision of 0.924, a recall of 0.993, an F1-measure of 0.957 and a mean average precision (mAP) of 0.99. In addition, dialogue recognition in three daily conversation scenarios was performed and evaluated. The average word error rate (WER) was 0.235, 10% lower than in of other works. Our work provides an alternative form of sign language recognition and a new approach to simplify the training process and achieve a better continuous sign language recognition effect.

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