The distribution of grayscale values in calligraphic character document images exhibits significant variations under poor lighting conditions, resulting in lower image contrast in low-light areas and degradation of morphological texture features of the strokes. Traditional methods typically focus on local information such as mean, squared deviation, and entropy, while giving less consideration to morphological texture, rendering them insensitive to the features of low-contrast areas. To address these issues, this study proposes a binarization method called clustering segmentation-based side-window filter (CS-SWF) specifically designed for degraded calligraphic documents. Firstly, this method utilizes multi-dimensional SWF to describe pixel chunks with similar morphological features. Then, with multiple correction rules, it utilizes downsampling to extract low-latitude information and correct feature regions. Finally, the clustered blocks in the feature map are classified to obtain the binarization results. To evaluate the performance of the proposed method, it is compared with existing methods using F-measure (FM), peak signal-to-noise ratio (PSNR), and distance reciprocal distortion (DRD) as indicators. Experimental results on a self-constructed dataset consisting of 100 handwritten degraded document images demonstrate that the proposed binarization method exhibits greater stability in low-contrast dark regions and outperforms the comparison algorithm in terms of accuracy and robustness.