Enabling new interactions with library digital collections: automatic gender recognition in historical postcards via deep learning

Abstract

The Walter Havighurst Special Collections from University Archives & Preservation at Miami University's King Library has a growing collection of over 600,000 historical postcards, with approximately 30,000 digitized, primarily from the Midwest during 1890–1919. This collection supports various lines of inquiry from users, such as analyzing the evolution of gender portrayal in popular media in the United States. However, manually separating the collection into postcards of males and females would take thousands of hours, which prevents the library from supporting sociological analyses at scale. After assembling an open postcard dataset, we trained deep neural networks (i.e., YOLOv5x object detection models) to automatically detect people and classify them as male or female. Our approach limited biases in favor of one outcome by balancing the number of males and females via multi-label stratified 10-fold cross-validation. We showed that this approach can accurately detect and classify females and confidently detect and label males for the library's collection of historical postcards. Our precision of 94.9 % and recall of 33.0 % from 1890 to 1919 on male gender detection exceed the performances of 94.7 % and 31 % respectively for recognition on World War I postcards in past studies. By employing our trained deep neural networks, the library can enhance its metadata within hours and support new research inquiries at scale.