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A certain naval force used manual check-in for missiles in and out of the warehouse, which caused a lot of manpower and time to be wasted during the warehouse. Using deep learning, automatic registration can be achieved. By adding asymmetric convolution to the CRNN algorithm, the width of the perception area is increased, and the Attention mechanism is added to perform a weighted average of the feature sequence. Through experimental comparative analysis on artificially synthesized data sets, the accuracy and the loss of the target missile number model based on the improved CRNN algorithm have achieved better performance. Compared with other advanced text recognition algorithms, its character accuracy has reached 98.9%, and its average editing distance is as low as 0.92. And it can accurately identify the missile number after actual testing. Therefore, it is feasible to use the improved CRNN algorithm to identify the missile number and assist the staff in the automatic registration of missiles in and out of the warehouse.