English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we approach the problem of segmentation-free query-by-stringword spotting for handwritten documents. In other words, we use methodsinspired from computer vision and machine learning to search for words in largecollections of digitized manuscripts. In particular, we are interested inhistorical handwritten texts, which are often far more challenging than modernprinted documents. This task is important, as it provides people with a way toquickly find what they are looking for in large collections that are tediousand difficult to read manually. To this end, we introduce an end-to-endtrainable model based on deep neural networks that we call Ctrl-F-Net. Given afull manuscript page, the model simultaneously generates region proposals, andembeds these into a distributed word embedding space, where searches areperformed. We evaluate the model on common benchmarks for handwritten wordspotting, outperforming the previous state-of-the-art segmentation-freeapproaches by a large margin, and in some cases even segmentation-basedapproaches. One interesting real-life application of our approach is to helphistorians to find and count specific words in court records that are relatedto women's sustenance activities and division of labor. We provide promisingpreliminary experiments that validate our method on this task.

Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections