# A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference
## 2 Literature Review
### CCL and ACCR  
CCL generates a bounding box for the container code area in raw image. The output of this step has a great impact on the accuracy of CCR. Existing CCL algorithms can be divided into three categories: edge-based, MSER-based and DL-based (deep learning) [25]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **CCL** & **CCR** & **Success Rate** & **Time** \\ \hline [PERSON] and [PERSON] 2013 [6] & Edge-based & Back Propagation Neural & & \\  & & Network (BPNN) & 98.39\% & 240 ms \\ \hline [PERSON] et al., 2016 [16] & Appearance-based & Support Vector Machine & & \\  & & (SVM) & 96.00\% & 580 ms \\ \hline [PERSON] et al., 2016 [17] & Edge-based & \begin{tabular}{c} LeNet-5 and Template \\ Matching (TM) \\ \end{tabular} & 94.70\% & 920 ms \\ \hline [PERSON] et al., 2018 [18] & Connected components & LeNet-5 & 95.41\% & 10,000 ms \\ \hline  & Maximally Stable External & & & \\  & Regions (MSER) and & Convolutional Recurrent & & \\  & Connectionist Text Proposal & Neural Network (CRNN) & 93\% & - \\  & Network (CTPN) & & & \\ \hline [PERSON] et al., 2018 [7] & - & Broad Learning System (BLS) & 97.85\% & 20 ms \\ \hline [PERSON] et al., 2019 [9] & MSER-based & \begin{tabular}{c} Convolutional Neural \\ Networks (CNN) \\ \end{tabular} & 96.30\% & - \\ \hline [PERSON] et al., 2020 [19] & MSER-based & LeNet-5 and TM & 99.30\% & 1800 ms \\ \hline [PERSON] et al., 2020 [20] & ResNet and U-net & CRNN & 93.33\% & 885 ms \\ \hline [PERSON] et al., 2020 [21] & Edge-based & BPNN & 96.80\% & 2000 ms \\ \hline Chun-ming 2020 [10] & \begin{tabular}{c} Single Shot MultiBox Detector \\ (SSD) \\ \end{tabular} & SVM & 94.60\% & 630 ms \\ \hline [PERSON] et al., 2020 [15] & \begin{tabular}{c} You Only Look Once V3 \\ (YOLOv3) \\ \end{tabular} & CRNN & 96\% & 159 ms \\ \hline [PERSON] et al., 2020 [22] & \begin{tabular}{c} Efficient and Accuracy Scene \\ Text (EAST) \\ \end{tabular} & - & 96\% & 52 ms \\ \hline [PERSON] et al., 2021 [23] & \
\begin{tabular}{c} Differentiable Binarization \\ (DBNet) \\ \end{tabular} &
\begin{tabular}{c} Show, Attend and Read & 95\% & 200 ms \\ \hline [PERSON] et al., 2022 [24] & Edge-based & SVM & 97.30\% & 100 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related research. Container code characters have strong vertical edges, so vertical edge detection can be used to locate container code area. [PERSON] et al. (2017) and [PERSON] et al. (2014) use the filter to detect vertical edges on the image and then use connection component analysis to remove noisy edges and locate the container code area. [PERSON] and [PERSON] (2018) use the edge detection operators to detect the vertical edge and then use the sliding windows to find the distinguishing edge features to complete the location of the container code area. [PERSON] et al. (2019) proposed a centroid-based vector regression method for generating text region quadrilateral bounding boxes, which exhibits high generalization for text regions of different shapes and aspect ratios. Edge-based CCL is easy to implement and computationally fast, but it cannot be used in polluted or inclined containers. Further, this method is very sensitive to illumination changes. Some researchers use MSER to locate container code area. [PERSON] et al. (2018) use MSER to detect areas where characters may exist then use the geometric clustering algorithm and the spatial structure template to identify the container code area. [PERSON] et al. (2019) combine Connectionist Text Proposal Network (CTPN) with MSER to improve the locating accuracy, but the algorithm is complex. [PERSON] et al. (2019) propose a CCL method combining MSER and edge, which achieved good results. Although MSER-based CCL has a good locating effect on images with a rusty container number, the reliability of this algorithm is insufficient in an uneven lighting environment. Recently, the DL-based CCL has gradually become the mainstream.