A New Framework for Container Code Recognition by Using Segmentation-Based and HMM-Based Approaches

作者：Wu, Wei^*; Liu, Zheng; Chen, Mo; Liu, Zhiming; Wu, Xi; He, Xiaohai

来源：International Journal of Pattern Recognition and Artificial Intelligence, 2015, 29(1): 1550004.

DOI：10.1142/S0218001415500044

• Container code recognition
• character recognition
• hidden Markov model
• sliding window

Traditional methods for automatic recognition of container code in visual images are based on segmentation and recognition of isolated characters. However, when the segment fails to separate each character from the others, those methods will not function properly. Sometimes the container code characters are printed or arranged very closely, which makes it a challenge to isolate each character. To address this issue, a new framework for automatic container code recognition (ACCR) in visual images is proposed in this paper. In this framework, code-character regions are first located by applying a horizontal high-pass filter and scan line analysis. Then, character blocks are extracted from the code-character regions and further classified into two categories, i.e. single-character block and multi-character block. Finally, a segmentation-based approach is implemented for recognition of the characters in single-character blocks, and a hidden Markov model (HMM)-based method is proposed for the multi-character blocks. The experimental results demonstrate the effectiveness of the proposed method, which can successfully recognize the container code with closely arranged characters.

• 出版日期2015-2
• 单位成都信息工程大学; 四川大学