ASAHmap: An Adaptive Chinese Handwritten Character Segmentation Algorithm for Large-Scale Ancient Handwritten Document Based on Histogram Projection and Gaussian Kernel Convolution Map

This paper presents a method for recognizing hand-written Chinese characters on ancient documents using optical character recognition (OCR). The method employs a segmentation algorithm based on histogram projection and Gaussian kernel convolution map, which accurately divides images of characters into sub-images of a single character. The algorithm was tested on a dataset of over one million Chinese handwritten characters and achieved a segmentation accuracy of over 97.75% in the best-case scenario for four categories of ancient documents. The proposed method provides a lightweight preprocessing technique for subsequent work aimed at recognizing ancient Chinese handwritten character documents using a neural network.