# 1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
## 5 Region Extraction
### Region Estimation Network  
With the rotation angle \(\theta\) obtained in Section 4, we consider that the 1D barcode rotated back to normal can be approximately regarded as a rectangle in the image. The rectangle is also a minimum bounding rectangle of the 1D barcode region containing all the line segments belonging to the 1D barcode. We try to train a neural network to regress four values to define this rectangle region: Two ratio values of barcode length and height in an image, two offset values from the barcode rectangle center to the image center. We rotate the barcode image in \(I\) back by angle \(\theta\) as predicted in Section 4 and set it as the input of the region estimation network. The task of the region estimation network is the localization of the position of the 1D barcode rectangle and the calculation of its length and height, which helps to extract the correct line segments (line segments from the 1D barcode region) in \(L\). The line segment with the midpoint in this rectangle is selected as a barcode line segment. We do not use the output of the region estimation network directly as a result and only use it as a region selector for the line segments, because practical 1D barcodes may suffer distortion, which makes them look less like a rectangle.  
The training data of the region estimation network can be obtained from the same dataset used for training the object detection network in Section 4 with further processing. We describe the data processing procedure in Section 6.  
Figure 2: Angle prediction in the bounding box region: We detect all the line segments in the bounding box region, and calculate the angle and length of every line segment using the endpoints. Then, we cluster all the line segments and summarize the length in every degree. As the clustering result shows in the angle histogram, 53\({}^{\circ}\) is the maximum length value, which will be chosen as the rotation angle.  
We use the Resnet-18 network [19] as the mainframe of the region estimation network, and replace the last average pool layer with four filters to regress the values with activation functions ReLU (Rectified Linear Units). The loss function is defined as  
\[L=(w_{p}-w_{g})^{2}+(h_{p}-h_{g})^{2}+\xi((x_{p}-x_{g})^{2}+(y_{p}-y_{g})^{2}), \tag{1}\]  
where \(w_{p}\) is the prediction of the ratio of the barcode rectangle width to the image width, \(h_{p}\) is the predicted ratio of the barcode rectangle height to the image height, \(x_{p},y_{p}\) are the predicted offset values of the barcode rectangle center point to image center point. \(\xi\) defines the weight loss of the offset, we set it to 0.1. \(w_{g},h_{g},x_{g},y_{g}\) are the ground-truths.  
We designed our network to learn to locate the rectangle region of the 1D barcode by describing the area and offsets, which provides a rectangle region used to judge the line segments to be selected or not. For every line segment in \(L\), we check its midpoint position in the rotated image. If the midpoint is still in the rectangle region estimated by the region estimation network, we select this line segment as a barcode line segment. All selected line segments should be collected for accurate region proposal. Figure 3 describes the detail of the line segment selection procedure.