# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 3 Proposed Framework
### The ENBC System
#### 3.2.1 Encoder  
Text recognition relies heavily on character context information. However, the size, form, and position of writing in natural settings vary widely. Previous techniques often integrate contextual information via consecutive pooling or other downsampling layers, resulting in a loss of resolution. Furthermore, the idea of receptive fields in convolutional neural networks is quite significant. When the receptive field is too tiny in comparison to the segmented picture, it captures the image's local information, and if the global information is not present, it causes various levels of segmentation problems. As a result, the size of the receptive field directly influences how much image feature information is captured, affecting reconstruction outcomes. Traditional recognition techniques with inadequately large receptive fields will result in decreased recognition accuracy for longer or bigger texts. Instead, the ENBC network captures information at various sizes using atrous convolution and atrous spatial pyramid pooling (ASPP). Dilated convolutions broaden the receptive field by introducing gaps (dilations) between the convolution kernel's components, enhancing information characteristics between characters. At the same time, this process must ensure that both the processing cost and parameter count remain constant. This empowers the system to collect more character context information while still keeping high-resolution features. Assuming \(k\times k\) is the size of the convolution kernel, \(H\times W\) is the dilation rate, and \(y[i,j]\) is the size of the input feature map, the result of the convolution operation may be stated as:  
\[y[i,j]=\sum_{m=1}^{k}\sum_{n=1}^{k}x[i+r\times(m-1),j+r\times(n-1)]\cdot w[m,n] \tag{1}\]  
Among them, \(w\) is the weight of the convolution kernel; \(x\) is the input feature map; \(y\) is the output feature map; and \(r\) is the expansion rate, which governs the spacing of elements in the convolution kernel and defines the size of the receptive field. As a result, null convolution is utilized to produce features with the same size but distinct receptive fields. The emergence of null convolution [54] can solve the contradiction between resolution and receptive field in the pooling layer, and the resulting feature mapping can have the same size as the input image while increasing the receptive field without increasing the number of convolution kernels, which is increasingly being used by researchers. The idea of dilatation rate is introduced in null convolution, as seen schematically in Figure 4 below. Figure 4a depicts a \(3\times 3\) kernel and a \(1\) expansion rate, also known as conventional convolution. Figure 4b depicts a \(3\times 3\) kernel and a \(2\) expansion rate. For a \(7\times 7\) picture block, just \(9\) points and \(3\times 3\) convolution kernels are utilized for convolution, leaving the other points unaffected. Figure 4c illustrates a \(3\times 3\) kernel and a \(4\) expansion rate, resulting in a \(15\times 15\) receptive field. Dilated convolution can totally replace the pooling procedure,increasing the receptive field without affecting the picture quality, resulting in an output with a wide range of character image feature information. The ASPP module in the ENBC network extends the output feature mapping to cover larger scales of sensory field sizes by applying several null convolution layers in parallel, each with a distinct expansion rate, as illustrated in Figure 5. The ASPP has five branches, including a \(1\times 1\) convolution, three cavity convolutions with variable expansion rates, and a global average pooling branch. Each branch represents a different scale, allowing for the acquisition of higher-level character picture information.