Bayesian.Ninja

The OCR task can be broken down as follows. Acquire the image. Segment it into regions according to the following labels:  Image,  Text Areas with optional rotation Tabular Data with optional rotation Scale down very large text to  suitable size glyphs Improve results by adding terms to better model the noise extant on page. Improve results by using lexical and grammatical knowledge into classifier. Ideally all this should be done by an end to end system. A common complex layout of Hebrew sacred texts with non rectangular columns with related but independent  sequences Once text area are detected requires a page segmentation algorithm to break down text areas into lines and glyphs.  Looking at some samples from the NLI ephemera database one would wish to add steps to clean up and rescale some elements whose fonts are too small. Also if one had a suitable model, perhaps add details to text that is too small. The challenges are...

About the event  The happy winners! Amir Aharoi, Oren Bochman and Chaim Cohen Last week (November 23-24 2017) I had the pleasure of participating in the first  National Library of Israel's Hackathon . I've been to the NLI a few times with friends from the Wikimedia movement to instruct its staff and students about editing Wikipedia. But at the hackathon, the NLI opened its doors to the best and brightest minds to help out with tagging content and dissemination of its extensive image database. The Team You can't win a hackathon without a great team. My team consisted of seven developers which have been a part of the core community of Wikimedia developers in Israel and have been meeting irregularly since the International Wikimedia hackathon Organized by Wikimedia Israel last year in Israel.  We had met about a week before the event at the Local chapter's offices and discussed over pizza what we wanted to do and what the NLI had asked us to do. I realized that ...