We wanted to develop a reasonably simple method to evaluate software in a way that made sense for our digitization projects. We wanted to be able to reproduce this method when we had a new type of material to digitize, and to re-run the test a few years in the future to determine if the “best” software had changed. We considered our digitization project needs. Our Archives and Special Collections Team is interested in creating searchable PDFs from scanned, typed documents such as journals, newspapers, and books. These PDFs are accessed by our users through our digital repositories such as Omeka, Open Journal Systems, and our institutional repository that runs on Digital Commons. Some of our items are scanned in-house, and some scanning is outsourced. We are also currently scanning handwritten material, but that is outside the scope of this paper - our archivists are piloting a user transcription project for handwritten documents such as diaries. For this project, we are not looking at the METS/ALTO XML standards for digitization projects, though we are hoping to explore this in the future.įor this study, we determined that we wanted to know how OCR programs facilitate searching and accessibility of documents in a repository or database, replicating how our users would interact with the documents online.

We examined how well these programs recognize individual words and how well they handle page segmentation. We tested three (and a half) popular OCR programs: ABBYY Finereader (and its Hot Folders option), Adobe Acrobat Pro, and Tesseract. We also looked briefly at two options from Google.

Free ocr software 2017