A semantic reasoning network, or SRN, is a framework designed for scene text recognition that is composed of four components.
Backbone Network
The backbone network is responsible for extracting 2D features from an input image, which are then used to generate 1-D features by the PVAM module.
Parallel Visual Attention Module (PVAM)
The PVAM module generates N aligned 1-D features G, where each feature corresponds to a character in the text and captures the aligned visual information. This means the module can detect and analyze every character in a given text string, no matter how many characters there are.
Global Semantic Reasoning Module (GSRM)
The GSRM is used to capture the semantic information S from the aligned visual features generated by the PVAM module. The module is essentially a middle step that takes the visual features and translates them into meaningful semantic information about the text string being analyzed.
Visual-Semantic Fusion Decoder (VSFD)
The final component, the VSFD, fuses the aligned visual features G and the semantic information S to predict N characters. This is done by analyzing the semantic information extracted by the GSRM and using it to improve the accuracy of the information gleaned from the aligned visual features generated by the PVAM module.
SRN is an end-to-end trainable framework which means that it can learn and improve over time as it is exposed to more data. It is commonly used in scene text recognition, where it can accurately detect and analyze text strings in a given scene and extract semantic information from them.
