TorchSharp.rar-Based Text Recognition Model: Exploring Fundamental Network Frameworks in Computer Vision

In the field of computer vision (CV), the cornerstone of image recognition and detection lies in feature extraction. Classical image classification models such as VGGNet, ResNet, InceptionNet (GoogleNet), DenseNet, Inside-Outside Net, and Se-Net serve as foundational networks or universal models for extracting features from input images. One prominent example is the Fully Convolutional Network (FCN), originally designed for semantic segmentation. FCN, devoid of fully connected layers, excels in extracting detailed image features. Leveraging techniques like deconvolution, upsampling, and sub-pixel convolution layers, FCN performs upsampling operations to restore feature matrices. The last layer of FCN generates a feature map with a high pixel resolution, making it suitable for tasks like scene text recognition, where clear stroke details are crucial for distinguishing characters, especially in Chinese characters. When applied to text recognition, FCN's final feature map classifies each pixel into two categories: text lines (foreground) and non-text regions (background).