A computer-implemented method of separating text and a figure of a document image, the method comprising: acquiring the document image; dividing the document image into a plurality of regions of interest by acquiring connection components of the document image and selecting boundary boxes corresponding to each connection component as the regions of interest; acquiring a plurality of feature vectors, wherein each feature vector is acquired by using a two-dimensional, 2D, histogram obtained by resizing one of the regions of interest among the plurality of the regions of interest to a preset size while maintaining the aspect ratio of the one of the regions of interest, and extracting a connection component of the resized region of interest; acquiring a plurality of transformation vectors of the plurality of feature vectors by transforming the plurality of feature vectors into first transformation vectors by using a predefined kernel-approximating feature mapping function; acquiring a plurality of cluster centers of the plurality of transformation vectors by performing first level clustering on the first transformation vectors; acquiring a plurality of superclusters by performing second level clustering on the plurality of cluster centers; and classifying each of the plurality of superclusters into one of a text class and a figure class based on the number of superclusters, wherein classifying each of the plurality of superclusters into one of a text class and a figure class, based on the number of superclusters, comprises: when the number of acquired superclusters is two, using a zero-shot label inference operation to classify each supercluster into one of the text class and the figure class, and when the number of acquired superclusters is more than two, using a semi-supervised label propagation operation to classify each supercluster into one of the text class and the figure class. acquiring the document image; dividing the document image into a plurality of regions of interest by acquiring connection components of the document image and selecting boundary boxes corresponding to each connection component as the regions of interest; acquiring a plurality of feature vectors, wherein each feature vector is acquired by using a two-dimensional, 2D, histogram obtained by resizing one of the regions of interest among the plurality of the regions of interest to a preset size while maintaining the aspect ratio of the one of the regions of interest, and extracting a connection component of the resized region of interest; acquiring a plurality of transformation vectors of the plurality of feature vectors by transforming the plurality of feature vectors into first transformation vectors by using a predefined kernel-approximating feature mapping function; acquiring a plurality of cluster centers of the plurality of transformation vectors by performing first level clustering on the first transformation vectors; acquiring a plurality of superclusters by performing second level clustering on the plurality of cluster centers; and classifying each of the plurality of superclusters into one of a text class and a figure class based on the number of superclusters, wherein classifying each of the plurality of superclusters into one of a text class and a figure class, based on the number of superclusters, comprises: when the number of acquired superclusters is two, using a zero-shot label inference operation to classify each supercluster into one of the text class and the figure class, and when the number of acquired superclusters is more than two, using a semi-supervised label propagation operation to classify each supercluster into one of the text class and the figure class. when the number of acquired superclusters is two, using a zero-shot label inference operation to classify each supercluster into one of the text class and the figure class, and when the number of acquired superclusters is more than two, using a semi-supervised label propagation operation to classify each supercluster into one of the text class and the figure class.