Auto-grading, as an instruction tool, could reduce teachers' workload, provide students with instant feedback and support highly personalized learning. Therefore, this topic attracts considerable attentions from researchers recently. To realize the automatic grading of handwritten chemistry assignments, the problem of chemical notations recognition should be solved first. The recent handwritten chemical notations recognition solutions belonging to the end-to-end trainable category suffered from

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... the problem of lacking the accurate alignment information between the input and output. They serve the aim of reading notations into electrical devices to better prepare relevant edocuments instead of auto-grading handwritten assignments. To tackle this limitation to enable the auto-grading of handwritten chemistry assignments at a fine-grained level. In this work, we propose a component-detectionbased approach for recognizing off-line handwritten Organic Cyclic Compound Structure Formulas (OCCSFs). Specifically, we define different components of OCCSFs as objects (including graphical objects and text objects), and adopt the deep learning detector to detect them. Then, regarding the detected text objects, we introduce an improved attention-based encoder-decoder model for text recognition. Finally, with these detection results and the geometric relationships of detected objects, this article designs a holistic algorithm for interpreting the spatial structure of handwritten OCCSFs. The proposed method is evaluated on a self-collected data set consisting of 3000 samples and achieves promising results.