In the field of handwriting analysis, deep neural networks seemed to perform not as well as the classic machine-learning methods (such as the support vector machine). Thus, we started the inspection from the source, that is raw data, the input of deep neural networks. One is random splits, it has a positive effect on test accuracy and loss, solving the small sample problem by creating more samples to make up for the lack of training samples. The other is preprocessing. We carried out experiments with and without preprocessing, and the results show that preprocessing operation is necessary especially binarization operations. Furthermore, the order of preprocessing operation also affects the final prediction result. Finally, we compared with state-of-the-art methods, and obtained competitive experimental results, discovering the power of micro-inspection in handwriting analysis.