# Fi-Fo Detector: Figure and Formula Detection Using Deformable Networks
## 2 Related Work
This work, however, can only be applied to PDF documents, which is a considerable limitation when we talk about document images. Deep learning, in the recent past, has become the center of attention for research in the document analysis community. [PERSON] et al. [11] and [PERSON] et al. [12] leveraged Faster-RCNN for tabledetection. [PERSON] et al. [3] additionally equipped the Faster-RCNN model with deformable property to gain significant improvements over prior state-of-the-art. NLPR-PAL [19] were the winners of the ICDAR-2017 competition on POD. They presented a multi-stage approach for classification of figures, formulas, and tables from document images using the connected components of the input image, SVM classifiers, CNN based CRFs, Faster-RCNN, and normal CNNs. Lastly, final results are achieved by integrating the intermediate results of these stages. A recent state-of-the-art POD system is proposed by [PERSON] et al. [9]. They proposed a hybrid model, which is a combination of deep structured prediction and supervised clustering for page object detection. First of all, they extract columns and then line regions of document images. They used conditional random fields formulated by a convolution neural network with unary and pairwise potentials to classify and cluster primitive region proposals from line regions. After classification, the same class clusters are merged to get page objects. Their presented approach is comprised of partially trainable networks with heuristics driven pre-processing and post-processing heads, which might limit its application in a generic scenario. [PERSON] et al. [20] presented the most recent method for page object detection in document images. Their approach is based on mask-RCNN for figure, formula, and table detection in document images. Although the presented approach has no strings (pre and'or post-processing) attached to it, they did not include compared their approach against the state-of-the-art results [9]. Moreover, they used different evaluation metrics rather than following the standards introduced in the ICDAR-2017 POD competition for page object detection. Thus their approach is not considered for comparison in this work. Recently, systems have been presented for parsing, classifying, and localizing figures from PDF documents. [PERSON] et al. [21] presented a method to parse figures from PDF documents, parsed figures are then classified using graph-based CNNs. They also present a figure classification dataset namely \"FigureSeer\". [PERSON] et al. [22] present another method \"PDFFigures 2.0\" to parse and classify figures from PDF documents along with a new dataset. [PERSON] et al. [23] present \"DeepFigures\" a deep neural method for detecting figures from PDF documents. These methods deal with PDF documents but not document images as in our case, so are out of scope for comparison in the presented work. In this paper, we will be mainly comparing against the state-of-the-art system by [PERSON] et al. [9] and with NLRP-PAL [19], i.e., the winner of the ICDAR-2017 competition on page object detection from document images.