This paper examines the impact of input data structure on deep learning models for handwriting analysis, focusing on the concept of “granularity.” Granularity represents the degree of segmentation or the perspective through which models process input data. Our findings show that splitting operations not only alleviate the issue of small sample sizes but also help identify the ideal zoom scale for model training. The key to achieving optimal granularity lies in balancing detailed local information with broader global patterns, rather than simply increasing the number of data splits. When selected effectively, granularity can reduce the dependency on additional training samples. Our experimental results indicate that the optimal granularities are 3 \(\,\times \,\) 8 (horizontal strips), 8 \(\,\times \,\) 6 (vertical strips), and 9 \(\,\times \,\) 12 (square segments). Based on these findings, we recommend that handwriting analysis training samples consist of 3 to 4 text lines, each containing more than 2 words per line, with training sample sizes ranging from 77 to 1506.