Handwritten Chinese text line recognition and handwriting verification play a crucial role in various fields such as office automation, classification of anonymous letters, and identity authentication. However, existing algorithms face significant challenges in extracting features from handwritten Chinese characters due to the complexity of their structure, image distortion and blurriness, as well as the limited availability of data samples. Furthermore, most existing studies focus on recognizing individual characters and verifying handwritten signatures. However, in practical applications, Chinese handwriting typically appears in text line format. To address these challenges, we construct the HW-CN dataset and propose an adaptive image interpolation algorithm (Otsu-Better) to tackle problems such as broken strokes and blurred characters in low-resolution images. Additionally, we introduce a recognition model specifically designed for handwritten Chinese text lines, which we refer to as Handwritten Chinese Text Line Recognition (HCTLR), to better meet the demands of real-world scenarios and reduce the impact caused by text segmentation. Experimental results demonstrate that the proposed HCTLR model achieves a total recognition accuracy of 78.7% in the HW-CN dataset, representing an improvement of 6.3% points compared to the CRNN model.