Developmental coordination disorder (DCD) is a type of motor learning difficulty that affects five to six percent of school-aged children, which may have a negative impact on the life of the sufferers. Timely and objective diagnosis of DCD are important for the success of the intervention. The present evaluation methods of DCD rely heavily on the observational analysis of occupational therapists and physiotherapists, who score the performance when children conduct some designed tasks. However, these methods are expensive, subjective, and are not easy to expand to a larger population. A fine motor evaluation system (FMES) is proposed with two views of cameras to record children's performance, when they carry out three fine motor tasks. Automated algorithms are developed to perform automated scoring of fine motor skill. The automated algorithms include task localization and individual task evaluation. The purpose of task localization is to detect each task and extract segments belonging to each task from the original video that includes multiple segments of different tasks. A convolutional neural network with temporal filtering is used to do frame-wise classification, and a boundary localization algorithm is proposed to localize each task segment. For individual task evaluation, the extracted video segments of task 1 and task 2 are evaluated based on the proposed feature extraction and time positioning algorithm, and the paper drawings of task 3 are evaluated based on image processing. The proposed methods are validated on a diverse population of children with or without DCD by comparing automated scoring with manual scoring from a professional evaluator. The experimental results suggest that the proposed methods can effectively achieve fine motor evaluation for DCD assessment. Besides, our system is a low-cost solution, and the evaluation methods developed are automated, objective, and can be suited for large population evaluation and analysis.