Table detection played a vital role in scanned document mining and understanding. The complex nature of tables has made table detection cumbersome and resource-intensive. In this paper, a novel two-path semi-supervised single-shot object detection framework was proposed for automatic detection of table in scanned documents. The proposed framework includes a shared VGG16 network and a two-path single-shot object detection model comprising of supervised and unsupervised subnetworks for table detection and classification. CascadeTabNet General dataset was employed to validate the effectiveness of the proposed framework. Experimental results demonstrated that the model implemented under the proposed framework is robust across various amount of available annotated documents (labeled data) for training. It can detect and classify tables in scanned document effectively even when training on very limited labeled data. For example, the precision of the proposed model is within 3% of a supervised model (SSD300) when training on only 10% of labeled data and 90% of unlabeled data.