DocEmul: a Toolkit to Generate Structured Historical Documents
This addresses the data scarcity problem for researchers and practitioners in document analysis, particularly for historical handwritten collections, though it is an incremental improvement over existing synthetic data generation methods.
The authors tackled the problem of limited training data for document analysis tasks by developing DocEmul, a toolkit that generates structured synthetic historical documents. They used it to create larger datasets for training a convolutional neural network, achieving record counting on handwritten collections with improved performance through data augmentation.
We propose a toolkit to generate structured synthetic documents emulating the actual document production process. Synthetic documents can be used to train systems to perform document analysis tasks. In our case we address the record counting task on handwritten structured collections containing a limited number of examples. Using the DocEmul toolkit we can generate a larger dataset to train a deep architecture to predict the number of records for each page. The toolkit is able to generate synthetic collections and also perform data augmentation to create a larger trainable dataset. It includes one method to extract the page background from real pages which can be used as a substrate where records can be written on the basis of variable structures and using cursive fonts. Moreover, it is possible to extend the synthetic collection by adding random noise, page rotations, and other visual variations. We performed some experiments on two different handwritten collections using the toolkit to generate synthetic data to train a Convolutional Neural Network able to count the number of records in the real collections.