A CRNN‐based method for Chinese ship license plate recognition

Abstract

Abstract Existing deep learning methods cannot achieve satisfactory ship license plate (SLP) recognition due to the harsh marine environment, such as foggy weather, unstable ship state and small targets. Therefore, a convolutional recurrent neural network (CRNN)‐based method is proposed for accurate SLP image recognition. Overall, the suggested method improves a CRNN recognition model by SLP image enhancement and data augmentation. The SLP image enhancement employs dark channel prior and Hough transform line detector to address the fog/blurriness and tilt issues existing in SLP images. As separate and joint operations, the two enhancements contribute to data augmentation for CRNN recognition. Preprocessing algorithms, including adaptive histogram equalization and image edge padding, are used to improve and unify the enlarged dataset for augmenting the CRNN model. As a final step, correction of the CRNN recognition results is made according to the character rule of SLPs, using an edit‐distance algorithm to match against a pre‐established SLP dictionary. A variety of real SLP images were collected to build an SLP image dataset for verification. The experimental results indicate that our method can reach an SLP recognition accuracy of ∼93%, which is significantly superior to other text‐based deep learning methods.

Keywords

• character recognition
• image enhancement
• learning (artificial intelligence)