Given the unrelenting pace of science and scientific publication, simply keeping up with the work in a highly active field can be a daunting challenge. Researchers increasingly rely on automated techniques to organize, browse, and search through scientific publications. While modern information retrieval algorithms can be very successful at analyzing the textual content of papers, making sense of the other less-structured components of the literature remains a challenge.
For example, scientific papers include a variety of figures, diagrams, tables, photographs, plots, and other less structured elements. These elements are often crucial to understanding the meaning and potential impact of a paper: a recent study discovered a significant correlation between properties of a paper’s figures and its scientific impact (citation count) in a large-scale dataset of biomedical literature , for instance. A variety of specific tasks within this general problem area have been studied, including chart understanding , figure classification [3, 4], graphical information extraction [5, 6], and pseudo-code detection .
Many figures in scientific papers (over 30% [1, 8]) consist of multiple subfigures, and so an important preliminary step is to segment or partition them into their individual components. Most existing work on figure separation relies on manually defined rules and features [9, 10, 11]
. These techniques are typically successful for the particular types of figures for which they were designed, but suffer from the classic problems with rule-based approaches: they tend to be “brittle” because when rules are not satisfied for any given figure instance, the system fails. For example, an intuitively reasonable rule is to assume some minimum white space between figures, but a small percentage of compound figures do not satisfy this assumption and thus cause the segmentation algorithm to fail, which likely prevents all subsequent figure understanding steps from succeeding as well. This brittleness has driven the document recognition community, and the entire computer vision and pattern recognition communities in general, towards more data-driven approaches that are better able to handle the outliers and uncertainties that are inherent in visual data.
In this paper, we propose a data-driven approach to compound figure separation, eliminating the need for manually designed rules and features, by using state-of-the-art object detection methods based on Convolutional Neural Networks (CNNs) . This approach views the compound figure separation problem as a form of object detection to predict bounding boxes of subfigures 
, as opposed to locating the partition boundaries explicitly. However, CNNs require large amounts of annotated data, a challenge which we address in two ways. First, we use transfer learning to initialize our CNN with parameters that were trained on 1.2 million annotated natural images from ImageNet. While this idea of fine-tuning by initializing from a pre-trained model has become common practice in computer vision [14, 15], it is nevertheless surprising that a problem as different as compound figure partitioning could benefit from transfer learning of parameters from consumer images like ImageNet. Second, we augment our training dataset by “synthesizing” new compound figure exemplars by pasting subfigures onto blank images. We evaluate our approach on the ImageCLEF compound figure separation dataset , and empirically demonstrate its effectiveness over several baseline systems that use manually designed features and pipelines. Finally, we have developed and publicly released an easy-to-use version of our compound figure segmentation software via a project website, http://vision.soic.indiana.edu/figure-separator/. To our knowledge, few compound figure separation tools are publicly available, which we view as a key bottleneck for advancing research related to figure mining, especially for scientometric and bibliometric researchers who may lack computer vision expertise. We hope our software can push additional work in this area.
To summarize, our paper makes the following contributions:
We propose a data driven, CNN-based approach for compound figure separation, a problem which has traditionally been addressed with manually-designed pipelines;
We empirically demonstrate the effectiveness of this data-driven approach using transfer learning and synthesized compound figures; and
We have developed an easy-to-use, publicly-available compound figure separation tool in order to encourage figure mining research.
Ii Related Work
Ii-a Scientific Figure Mining
Understanding scientific figures has long been studied in the document analysis community. Classifying the type of individual figures is a fundamental problem[4, 3], for example, while other work attempts to understand figures and extract semantic information from them [2, 5, 6, 16, 17, 18]. Progress in this area has inspired research into mining information from figures in large-scale collections of scientific papers. A scalable framework to extract figures directly from PDFs has been proposed . A figure-oriented literature mining system called Viziometrics , for example, discovered that each scientific discipline has its own patterns in the usage of figures, suggesting that scientific fields develop their own “visual cultures.” However, most of the above work assumes that figures consist of one single, simple component, preventing compound figures that consist of multiple components (which make up at least 30% of figures in the literature ) from being successfully analyzed. Compound figures must first be separated into simpler component pieces, which is a major motivation for our work.
Ii-B Compound Figure Separation
This compound figure separation problem has been studied in several recent papers [17, 9, 10, 11]. Lee et al.  and Siegel et al.  use background color and layout patterns, for example, while spaces and lines between subfigures are used as cues by Taschwer et al. , and Li et al. 
use connected component analysis. No matter which specific features are used, these approaches are created through careful engineering using manually-designed rules and human-crafted features. In contrast, we adapt a completely data-driven approach that views compound figure segmentation as an object localization problem, and use modern Convolutional Neural Networks (CNNs) to estimate bounding boxes around each of a compound figure’s component parts. This approach avoids the need for manually-written rules, and instead just requires training data with bounding box annotations. This is advantageous because it avoids the “brittleness” of manually-designed recognition pipelines, which often make hidden assumptions that are easily violated in real instances. It also allows our approach to be easily customized to the “visual culture” of figures within any specific scientific domain simply by re-training the CNN on new training data, as opposed to having to re-engineer the system by hand. Finally, this approach raises the possibility of integrated classifiers that could perform compound figure separation and subfigure classification in one unified step, given enough annotated training data.
Ii-C Object Detection
Compound figure separation is essentially a particular instance of object (i.e. subfigure) localization. The state-of-the-art for object localization in computer vision uses deep Convolutional Neural Networks, and there are two broad types of popular approaches. The first is to generate many (thousands of) candidate bounding boxes for potential object instances in an image, and then use a CNN to classify each bounding box individually [20, 21, 22]. An alternative approach is to use a CNN to process a whole image, and predict classes and bounding box locations at the same time as a regression problem [23, 12, 24]. These approaches are usually faster than region-based techniques but with a modest decrease in detection performance. In this paper, we adapt this latter approach, and specifically YOLOv2 , because it is reported to be among the fastest and highest performing.
Iii Compound figure separation
We now describe our approach for compound figure separation. The heart of our approach is to view figure separation as an object localization problem, where the goal is to estimate bounding boxes for each of the subfigures of a compound figure, using Convolutional Neural Networks. After explaining the basic model, we discuss how to address the practical (but critical) problem of training a CNN for our problem given a limited quantity of training data.
Iii-a Convolutional Neural Network
Our general approach is to apply the You Only Look Once version 2 (YOLOv2) system  to our problem of subfigure detection, because it is fast, unified, and simple, but highly effective for object detection. Please see  for full details; here we briefly highlight the key properties of this technique. Unlike prior CNN-based techniques for object localization (e.g. [20, 21, 22]
), YOLO avoids the need for separate candidate generation and candidate classification stages, instead using a single network that takes an image as input and directly predicts bounding box locations as output. The CNN has 19 convolutional layers, 5 max-pooling layers, and a skipping connection in a similar manner to residual networks. No fully connected layers are included, so the resolution of the input image is unconstrained. This makes it possible to train on randomly resized images, giving the detector’s robustness to input image resolution. The CNN downsamples the image by a factor of 32. Each point in the final feature map predicts bounding boxes, confidences, and object classes, assuming that the object is centered at the corresponding receptive field in the input image.
We follow most of the same implementation settings proposed by the YOLOv2 authors 
. Briefly, we use stochastic gradient descent to train 160 epochs with learning rate of 0.001 decreased by a factor of 10 at epochs 60 and 90. We use a batch size of 64, weight decay of 0.0005, and momentum of 0.9. For the implementation, we use Darknet
, a fast and efficient library written in C. After training is done, we port the trained model into Tensorflow and use it as the backend of our figure separation tool because it is easier to customize using Python. Our default input resolution is pixels.
Iii-B Transfer Learning
Unfortunately, we found that the simple application of YOLOv2 to compound figure partitioning did not work well. The problem is that while deep learning with CNNs has had phenomenal success recently, it requires huge training datasets – typically hundreds of thousands to millions of images – which are not available for many problems. Fortunately, this problem can be at least partially addressed through transfer learning , where a classifier trained on one problem can be used as a starting point for a completely different problem. For example, Razavian et al.  demonstrate that once a CNN is trained to classify natural images into 1,000 categories using 1.2 million images from ImageNet , it can be used as a generic feature extractor for a variety of unrelated recognition tasks from natural images. Another common trick is to use the CNN parameters trained on ImageNet as initialization for re-training on a different problem, even in a very different domain like document images [29, 30, 17], even though ImageNet does not include any documents. Based on these reports, we hypothesized that initializing our CNN using ImageNet pre-trained weights may also be effective for compound figure separation problems, and empirically validated this hypothesis.
Iii-C Compound Figure Synthesis
We found that transfer learning did not completely solve the problem of limited training data, however. Although transfer learning helps the CNN initialize its weights to reasonable values, successful training still requires that the learning algorithm sees a diverse and realistic sample of compound figures. To augment the training set, we used our limited ground-truth training set of “real” compound figures to automatically generate a much larger set of synthetic yet realistic additional figures.
We tried two approaches. The first involves generating compound figures by simply pasting subfigures together in a random manner. We first create a blank image with a randomly-generated aspect ratio between 0.5 and 2.0. We then choose a subfigure at random from the “real” training set, and then rescale the figure with a random scaling factor (while making sure that the subfigure does not exceed the size of the blank image). Then we randomly select an empty spot within the compound figure (where empty means that the intersection over union (IOU) with existing subfigures is less than 0.05), and paste the subfigure at this position. If no such empty spot can be found, we end the synthesis. An example of a generated compound figure is shown in Figure 2.
Unfortunately, this technique for generating synthetic training examples did not improve the accuracy of the trained classifier. We thus tried a second technique that generated compound figures in a more structured way. Most scientific figures are not composed of randomly-arranged subfigures, of course, but instead tend to have a more structured layout so that the subfigures are aligned in a near grid-like pattern.
We thus first randomly choose a number of rows (between 3 and 7) and a random height for each row. Then for each row, we randomly choose a number of subfigures (between 1 and 7),111The maximum of 7 is an arbitrary choice, although we believe it to be reasonable because the maximum number of subfigures in our ground truth set of compound figures was about 40. and paste that number of randomly-selected subfigures (re-sized to fit the row height) in the row. To make the exemplars as difficult as possible, we do not add white space between the subfigures. We then randomly transpose rows and columns to generate additional multiple exemplars for each synthetic compound figure. Three examples of synthesized figures are shown in Figure 3. Finally, we add additional diversity with three additional manipulations: synthetic images are randomly inverted so that some have black backgrounds instead of white, we randomly apply color transformations to figures to create diversity in color, and we randomly flip figures horizontally to add diversity in the spatial dimension.
As we show in the next section, this technique for synthesizing training images significantly improved the performance of the trained detector. Of course, this is just one technique for generating synthetic compound figures, and we do not claim it to be the best.
Synthesizing compound figures may at first seem easy, but is actually difficult if we want to obtain ones similar to real figures without injecting harmful biases into the training set. Investigating how to better synthesize real figures is an interesting direction for future work.
We used the ImageCLEF Medical dataset , which (to our knowledge) is one of the largest available collections of figures with bounding box annotations. The dataset has two versions: the 2015 version has 3,403 training images and 3,381 test images, while the 2016 version is larger, with 6,783 training images and 1,614 test images. It is reported that the 2015 data is much easier than 2016 data , so we focus on the 2016 data except when comparing to baselines for which only 2015 results are available.
We evaluate accuracy using the same metrics defined by the ImageCLEF task . We briefly summarize the metrics here; please see [31, 10] for full details. For each compound figure, an accuracy ranging from 0 to 1 is defined as the number of correctly detected subfigures over the maximum of the number of ground-truth subfigures and the number of detected subfigures. A subfigure is considered to be correct if the area of overlap between the ground truth and detected boxes is greater than 0.66. Note that this scoring function penalizes not only missed or spuriously-detected subfigures, but also multiply-detected subfigures. The accuracy for a whole dataset is the average of the individual accuracies. We also evaluate using mean average precision (mAP) , which is a standard measure used in object detection and roughly corresponds to the area under the precision-recall curve.
|Lee et al. ||2016||0.566||0.824||0.378||—|
|Li et al. ||2016||0.844||/||/||—|
|Transfer + random syn||2016||0.842||0.873||0.726||0.746|
|Transfer + grid syn||2016||0.859||0.880||0.775||0.782|
|Taschwer et al. ||2015||0.849||/||/||—|
|Transfer + grid syn||2015||0.917||0.918||0.896||0.889|
— indicates an inapplicable metric because the method does not produce confidence values.
/ indicates a value not reported in the original paper and that no public implementation was available for us to compute it.
Results of our evaluation are shown in Table I for several different variants of our detector: Pure data was trained just on the “real” figures in the ground truth, Transfer was pre-trained using ImageNet and then trained on the ground truth figures, Transfer + random syn used transfer learning but also synthetic images using the first of the two techniques described in Section III, and Transfer + grid syn used transfer learning and augmented the training set with the grid-structured synthetic images. For the synthetic settings, we added a number of synthetic images equal to the number of ground truth images (i.e. we doubled the training set size).
We observe that pre-training yields a significant improvement, increasing mAP from 0.698 to 0.773. Adding random synthetic data slightly harms performance, but the grid-structured synthetic data yields a small additional improvement (0.782 vs 0.773). The precision-recall curves in Figure 4 show a similar story.
The table also compares with three previous algorithms as baselines: Lee et al. , which uses background color and layout patterns (and was used in a project mining millions of figures ), Li et al. , which uses connected component analysis and is reported to be the best-performing technique on the 2016 dataset, and Taschwer et al. , which cues on spaces between subfigures and line detection and is reported to give the highest accuracy on the 2015 dataset. The results show that our technique significantly outperforms all baselines according to all metrics.
Some randomly-sampled correct and incorrect results from our approach are shown in Figures 5 and 6, respectively, where red boxes indicate our detection results and the yellow boxes are the ground truth. As seen in Figure 5, our approach is robust to variations in background color and spaces between subfigures; for example, in panes A, B, D and F, subfigures are aligned with almost no spaces and sometimes with black backgrounds. Note the wide variety of different figure and subfigure types, layouts, and designs that our detector is able to handle. On the other hand, some kinds of compound figures do confuse our approach, especially when small and similar subfigures are aligned very closely. Examples include pane B of Figure 6, where many small black images appears in a grid, and G where many similar chemical compounds are densely aligned. Other errors happens when relatively large sub-components are split apart into multiple subfigures, such as the legend in pane E, or the “A” label in pane D. In many of these cases, the definition of what should constitute a component is ambiguous, with inconsistencies in the ground truth itself. For instance, the ground truth separates the results of a chemical experiment in Figure 6C, but not in a similar subfigure in the upper right of Figure 6B. We also note that ground truth annotations are occasionally incorrect such as in Figure 6A, where our algorithm produced much more reasonable results than the actual ground truth (but was thus penalized in the quantitative evaluation).
We now discuss important points about our approach and experiments, as well as interesting directions for future work.
V-a Compound Figure Synthesis
Our approach for synthesizing compound figures is simple and ad-hoc, and other approaches are certainly possible. It turned out to be surprisingly difficult to synthesize realistic compound figures; we observed that unrealistic synthetic training images (such as our first technique of randomly pasting subfigures) can actually confuse the training algorithm more than they help.
Future work could explore learned, data-driven approaches to figure synthesis, such as modeling the probability distribution of figure layout conditioned on neighboring figures, or on the type or subject of the publication. Once the parameters of such a generative model are estimated, we could sample from that distribution to synthesize layouts similar to the real ones.
V-B Figure Separation Tool
To our knowledge, implementations of only two figure mining tools [19, 17] are public, and no compound figure separation tool is available. In fact, one of the major difficulties we experienced in trying to compare our technique to existing baselines was this lack of publicly-available implementations. More importantly, this makes it difficult for scientometric researchers, who usually lack computer vision expertise, to work on figure mining research. This may explain why much work on mining scientific papers has ignored visual aspects  and mainly focused on data that is easier to use, such as citations , authors , and textual content 
. We believe that this is because of the much greater availability of tools for analyzing textual content in the form of easy-to-use natural language processing libraries.
To help correct this limited availability of practical tools, we have made our compound figure separation code publicly available at http://vision.soic.indiana.edu/figure-separator/ as an easy-to-use Python library. We hope this may help to foster figure mining research even outside the computer vision and document analysis communities. Of course, we are aware that separating compound figures is not sufficient to apply large scale figure mining, and that we also need other components such as figure type classifiers. Developing and releasing implementations of these components is important future work. In fact, CNN-based object detectors may be capable of performing figure separation and classification simultaneously.
V-C Limited data
Recent advances in computer vision are due, to a large extent, to the growing size of annotated training data; ImageNet, for example, has many millions of labeled images. We believe that the lack of annotated data is holding back scientific figure mining research. For example, the ImageCLEF Medical dataset , the largest available dataset for compound figure separation, has only 7,000 images for training, which is smaller than most modern object detection datasets such as PASCAL VOC  or MSCOCO . ImageCLEF ground truth also has some erroneous annotations for subfigure locations ; we found at least 10 erroneous annotations when performing our experiments (e.g. Figure 6A). Moreover, the ImageCLEF data only has bounding boxes and does not have subfigure type annotations.
An important goal for this community could be to build up a much larger size dataset, perhaps on the order of 100,000 scientific figures with semantic annotations, in order to further accelerate progress in this domain. Our tool could ease the manual labor involved in creating such a dataset by generating initial subfigure separation proposals which could then be corrected or refined by a human annotator.
Many existing techniques first classify figures into compound or simple, and then run the compound figure separation algorithm only on the compound figures, in part because the separation algorithm is relatively slow [10, 1]. For example, separation is reported to take 0.3 seconds per compound figure in Taschwer et al. . This two step approach can be dangerous because if a compound figure is not recognized correctly, the subfigures can never be extracted. In contrast, our CNN-based separation tool requires 0.12 seconds per figure on a single NVIDIA Titan X GPU, which we believe is fast enough to eliminate the need for compound figure recognition. This takes approximately 33 hours to separate a million compound figures, and could be trivially parallelized on multiple GPUs.
We introduced a data driven approach for compound figure separation, which in the past had been addressed by manually-designed features and rules. Modern machine learning, and in particular Convolutional Neural Networks, eliminate the need for manual engineering but require large annotated training datasets. We addressed this challenge through a combination of transfer learning and automatic synthesis of training exemplars: transfer learning takes advantage of the visual patterns learned from 1.2 million annotated images from ImageNet, while compound figure synthesis offers a way to increase the training data without additional annotation costs. Our experiments demonstrate that our approaches are effective and outperform previous work.
We also released an easy-to-use compound figure separation tool, and hope the tool will help push forward research into scientific figure mining.
We thank the authors of Lee et al.  for providing an implementation of their work, Eriya Terada for providing useful comments on our manuscript, Prof. Ying Ding for her support and advice, and the anonymous reviewers for their time and helpful feedback. Satoshi Tsutsui is supported by the Yoshida Scholarship Foundation in Japan. This work was also supported in part by the National Science Foundation (CAREER IIS-1253549) and NVidia, and used the Romeo FutureSystems Deep Learning facility, supported by Indiana University and NSF RaPyDLI grant 1439007.
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