In this article, we will explore a simple method using Python to define areas in the document image for OCR
As organizations everywhere look to digitize their operations, transforming physical documents into digital formats is a common low-hanging fruit to pick. This is usually done with Optical Character Recognition (OCR), where images of text (the scanned physical document) are converted into machine text, via one of several well-developed text-recognition algorithms. Document OCR performs best when working with printed text against a clean background, with consistent paragraphing and font size. In practice, this scenario is far from the norm. Invoices, forms and even identity documents have information scattered throughout the document space, making the task of digitally extracting relevant data somewhat more complicated. In this article, we will explore a simple method using Python to define areas in the document image for OCR. We will use an example of a document with information scattered throughout the document space — a passport. The following sample passport is placed within a white background, simulating a photocopied passport copy.
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