e.g., retyping or using voice input and (b) Assess what types of characters, diacritics, and physical documents hamper the use of OCR, and speculate on why. For the OCR task described here, then, there are two goals: (a) Learn how OCR works in order to assess which documents can best be captured automatically vs. And it provides them a chance to discuss the tool’s output and to hypothesize why it came about. It raises the issue of what text recognition does by making the case for the larger value in understanding the difference between a picture file and a section of recognized text. The lesson is meant to take students beyond just clicking on a button to initiate a particular software process. So determining whether they can capture the text from a JPEG or PDF is a valuable first step. Often, they are interested in inputting materials that are only in printed form, or perhaps in an older PDF format. To do so, they may scrape the web for data, or gather existing textbook examples. In the case of more contemporary sources, my students often work on compiling the texts of understudied languages that they are exploring, make plans to use language learner essays, or work from computer-mediated exchanges, such as Tweets and text messages. (2015) have also gathered together texts from these first two sources into several corpora of late modern English texts that can be obtained for free.

For compiling their own mini corpus of texts, they can use a number of available existing public domain text archives, such as Project Gutenberg and the Oxford Text archive. txt files that they will use later with concordancing software so that they can then go on to check for the distribution of particular words in their texts, with the goal of finding multiple examples of words in context. It is a first methods course in preparing. This assignment for upper division corpus linguistics students is one of a series of tasks that students do to obtain material for a specialized corpus they will use across several assignments. The intended outcome is for students to experience hands-on OCRing and then produce a reflective write-up comparing their OCR experiences and giving their hypotheses about when digitization work best. I have students try using this mode first, and I provide a particularly messy scanned text for them to work on, in conjunction with an easier text page of their choice. While most optical character recognition (OCR) programs allow users to automatically distill text from an image file, in our departmental computer lab we used a software program that also has a “learning” mode, which is meant to help the OCR algorithm learn the characteristics of a particular text, but can, in turn, also be deployed to help humans see how the software makes its recognition choices.

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