In this paper, we approach the problem of segmentationfree query-by-string word spotting for handwritten documents. In other words, we use methods inspired from computer vision and machine learning to search for words in large collections of digitized manuscripts. In particular, we are interested in historical handwritten texts, which are often far more challenging than modern printed documents. This task is important, as it provides people with a way to quickly find what they are looking for in large collections that are tedious and difficult to read manually. To this end, we introduce an end-to-end trainable model based on deep neural networks that we call Ctrl-F-Net. Given a full manuscript page, the model simultaneously generates region proposals, and embeds these into a distributed word embedding space, where searches are performed. We evaluate the model on common benchmarks for handwritten word spotting, outperforming the previous state-of-theart segmentation-free approaches by a large margin, and in some cases even segmentation-based approaches. One interesting real-life application of our approach is to help historians to find and count specific words in court records that are related to women's sustenance activities and division of labor. We provide promising preliminary experiments that validate our method on this task.

No abstract

No abstract

Based on the verb-oriented method and a unique collection of observations from court records, this article shows that both men and women did almost all categories of work in early modern Sweden. On the level of concrete tasks, however, there was both difference and similarity between the genders. Marital status exerted a strong influence on women's sustenance activities, creating a clear distinction between unmarried and ever-married women. These patterns were probably the effect of a labour legislation that forced young people without independent means to offer their bodies and time to masters and mistresses.

No abstract