# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 6 Product Label Analysis
### Text Detection
#### 6.1.2 Segmentation-Based Methods  
Even with post-processing techniques, regression-based methods have important difficulties in dealing with arbitrary shape text. For this reason, arbitrary shape text detectors have adopted segmentation-based methods to represent text regions that rely on instance-level and pixel-level features. For more complex text scenes, these methods usually outperform regression-based methods. Nevertheless, they require complex and time-consuming post-processing steps to produce the final detection result, especially to separate adjacent text instances very close to each other. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**ICDAR2015**} & \multicolumn{4}{c}{**SCUT-CTW1500**} & \multicolumn{4}{c}{**Text Style**} \\
**Methods** & **Recall** & **Precision** & **F-Measure** & **FFS** & **Recall** & **Precision** & **F-measure** & **FFS** & **Multi** & **Covered** \\ \hline \(R^{2}CNN\)[100] & 79.68\% & 85.62\% & 82.54\% & 0.44 & - & - & - & - & \(\surd\) & \(\times\) \\ RRPN[110] & 73.25\% & 82.17\% & 77.44\% & - & - & - & - & - & \(\surd\) & \(\times\) \\ RRD[111] & 79.00\% & 85.04\% & 82.20\% & 6.5 & - & - & - & - & \(\surd\) & \(\times\) \\ RRD[111] & 80.00\% & 88.00\% & 83.80\% & - & - & - & - & - & \(\surd\) & \(\times\) \\ EAST+PVANET2[113] & 73.47\% & 83.57\% & 78.20\% & 13.2 & - & - & - & - & \(\surd\) & \(\times\) \\ TextBoxes++[112] & 76.70\% & 87.20\% & 82.90\% & 11.6 & - & - & - & - & \(\surd\) & \(\times\) \\ TextBoxes++[112] & 78.50\% & 87.80\% & 82.90\% & 2.3 & - & - & - & - & \(\surd\) & \(\times\) \\ MOST[118] & 87.30\% & 89.1\% & 88.2\% & 10 & - & - & - & - & \(\surd\) & \(\times\) \\ SegLink[105] & 76.80\% & 73.10\% & 75.00\% & - & - & - & - & - & \(\surd\) & \(\times\) \\ SegLink++[114] & 80.37\% & 83.70\% & 82.00\% & 7.1 & 79.80\% & 82.80\% & 81.30\% & - & \(\surd\) & \(\surd\) \\ CounterNet[113] & 86.10\% & 87.60\% & 86.90\% & 3.5 & 84.10\% & 83.70\% & 83.90\% & 4.5 & \(\surd\) & \(\surd\) \\ Zhang et al. [116] & 84.69\% & 88.53\% & 86.50\% & - & 83.02\% & 85.93\% & 84.45\% & - & \(\surd\) & \(\surd\) \\ FCENE[117] & 84.20\% & 85.10\% & 84.60\% & - & 80.70\% & 85.70\% & 83.10\% & - & \(\surd\) & \(\surd\) \\ FCENE+DCN[117] & 82.60\% & 90.10\% & 86.20\% & - & 83.40\% & 87.60\% & 85.50\% & - & \(\surd\) & \(\surd\) \\ \hline TextBoxes[119] & 80.40\% & 84.90\% & 82.60\% & 1.1 & 63.40\% & 65.40\% & 64.40\% & - & \(\surd\) & \(\surd\) \\ LOMO[120] & 83.50\% & 91.30\% & 87.20\% & - & 69.60\% & 89.20\% & 78.40\% & - & \(\surd\) & \(\surd\) \\ LOMO-MS[120] & 87.60\% & 87.80\% & 87.70\% & - & 76.50\% & 85.70\% & 80.00\% & - & \(\surd\) & \(\surd\) \\ PSNet+Is[121] & 84.50\% & 86.90\% & 85.07\% & 1.6 & 79.70\% & 84.80\% & 82.20\% & 8.4 & \(\surd\) & \(\surd\) \\ CRAFT[122] & 84.30\% & 89.80\% & 86.90\% & - & 81.10\% & 86.00\% & 83.50\% & 8.1 & \(\surd\) & \(\surd\) \\ PAN[123] & 81.90\% & 84.00\% & 82.90\% & **26.1** & 81.50\% & 85.00\% & 83.50\% & **8.1** & \(\surd\) & \(\surd\) \\ DBNet[124] & 83.20\% & 91.00\% & 87.30\% & 12 & 80.20\% & 86.90\% & 83.40\% & 22 & \(\surd\) & \(\surd\) \\ DBNet[145] & 83.90\% & 90.50\% & 87.30\% & 10 & 82.80\% & 87.90\% & 85.30\% & 21 & \(\surd\) & \(\surd\) \\ MOST[126] & 84.5\% & 92.50\% & **88.35\%** & 5 & - & - & - & \(\surd\) & \(\surd\) \\ RSCA[127] & 82.70\% & 87.20\% & 84.90\% & 23.3 & 83.30\% & 86.60\% & 85.0 TextSnake [119] predicts text of any shape considering text instances as a series of sequence disks. The geometry attributes of text instances are estimated via a Fully Convolutional Network (FCN). LOMO [120] also considered geometric properties to locate the characters and obtain quadrangular text regions. PSENet [121] implemented the progressive scale expansion network to solve the problem of closely adjacent text instances. CRAFT [122] was based on character awareness, predicting individual characters and their affinity to construct word bounding boxes. PAN [123] is a light computational detector that used a Pixel Aggregation network to aggregate text pixels precisely.