The Notary in the Haystack -- Countering Class Imbalance in Document Processing with CNNs
This work addresses class imbalance in document classification and segmentation for historical document analysis, but it is incremental as it applies known techniques to a specific domain.
The paper tackled class imbalance in document processing by evaluating countermeasures like data augmentation and oversampling for binary classification and segmentation tasks on medieval documents, achieving best performance with random minority oversampling plus data augmentation in classification and sufficient segmentation using class-weighted dice loss.
Notarial instruments are a category of documents. A notarial instrument can be distinguished from other documents by its notary sign, a prominent symbol in the certificate, which also allows to identify the document's issuer. Naturally, notarial instruments are underrepresented in regard to other documents. This makes a classification difficult because class imbalance in training data worsens the performance of Convolutional Neural Networks. In this work, we evaluate different countermeasures for this problem. They are applied to a binary classification and a segmentation task on a collection of medieval documents. In classification, notarial instruments are distinguished from other documents, while the notary sign is separated from the certificate in the segmentation task. We evaluate different techniques, such as data augmentation, under- and oversampling, as well as regularizing with focal loss. The combination of random minority oversampling and data augmentation leads to the best performance. In segmentation, we evaluate three loss-functions and their combinations, where only class-weighted dice loss was able to segment the notary sign sufficiently.