Impact of Convolutional Kernel Initialization based on Pixel Distribution Features on Table Recognition Performance

Due to the powerful feature learning and classification capabilities of convolutional neural networks, they have been widely applied in fields such as image recognition and classification, object detection, and semantic segmentation. Therefore, enhancing the efficiency and accuracy of convolutional neural networks in deep learning has become increasingly important. In convolutional neural networks, the initial design of the convolutional kernels significantly influences the efficiency of deep learning iterations. This paper focuses on the recognition and classification of image tables, proposing a method of customizing the initialization of convolutional kernel parameters based on several local elements of images and the similarity between their pixel distribution features and the initial parameters within the convolutional kernels. The deep learning of image tables through convolutional neural networks is then conducted on this basis, and compared experimentally with traditional initialization methods such as Normal and Xavier. The experimental results show that the parameter initialization method proposed in this paper achieves notably higher classification accuracy in the early stages of neural network training and overall higher classification accuracy for table recognition.