Already digital but still OCRed

May 19
2016

I’ve faced unique projects in the last four years and in a few, the best approach even seemed to contradict my better logic. The projects I’m talking about are ones where the data we were working with was already in a digital format, namely a PDF file that was created digitally. What this meant was that all the text in the PDF was available and 100% accurate. So why then, to accomplish the project’s goals, did we use OCR to read the already digital files as images?

I had intended for all these projects to do a logical parsing of the already digital content so I can get what I want. The problem is that even though the internal structure of the PDF has a logical standard, it’s not used logically 90% of the time by most PDF generating applications. PDF has in it a tolerance for mistakes that allows organizations to deviate quite drastically from the standard. What this means is that not only is the content in each PDF unique per company that generates it, it’s unique per number of applications able to create them. Variations on-top of variations makes logical parsing very difficult. This becomes most obvious when the documents contain tables. Because of this the only way to text parse the PDF properly would be to flatten the internal logic so that they consist of nothing but text, but by doing so you lose some of the information pointing to where tables are and their structure.

You may have guessed by now that all my projects were to parse tables from PDF. Not just any table but specific tables in PDFs where each was a unique format. As I said before, my preference would have been to use the 100% accurate data already in the PDF. In the end what I ended up doing was OCRing the PDFs because they were what is called “pixel perfect” so the accuracy was very high. Now that I was using OCR, I was able to first recognize an entire document and remove everything that was not a table which was determined by my OCR document analysis. Then I was able to use keywords to find the specific table that I wanted. The end result took me about 3 weeks of work for each project, and the result was higher accuracy in table finding, and only slightly less accurate in the text values than a table parsing.

While it seemed most logical to do the parsing, in the end I saved over 5 man-months of work by using OCR.

Chris Riley – About

Find much more about document technologies at www.cvisiontech.com.

Print to OCR?

Jun 16
2015

When I talk to people about the unique technique of printing text documents to image just for the purpose of running optical character recognition ( OCR ) or data capture on them, they are rightfully confused and think I’m a little nutz.

Why would you ever convert an already digital document back to image? I promise it’s not because I’m so fond of OCR; it actually has its purpose.

Language Detection: By converting a document to image for OCR, I can check the language of each word in the document. While I would much prefer to use a language detection tool on a digital file, there is no robust tool that exists to do this at volume. The unique aspect of OCR engines is that they contain morphology and dictionaries. This is where OCR has improved its accuracy in the past 5 years. OCR engines attempt to identify the language of text in order to better read the document. Because this mechanism is already built into the engine, if I convert a digital file to image and OCR it, I can tell you what languages exist in that document. Additionally, while font is a clear indicator of language, if it is not accompanied by the proper language encoding, it will not tell the digital process what a language is, and in OCR there is no need for such an encoding.

Normalization of digital formats: While a PDF created in Acrobat and a PDF created in a third party tool look identical to the viewer, internally these PDF files are very different. In order to accurately digitally parse a PDF file, you have to have a standard format that is used. If you do not have a standard format, you are dealing with variations in the document visually and its infrastructure. This becomes an overwhelming number of variations. For example, a collection of invoices has as many variations as there are invoices’ times as many PDF generating applications exist. However, if you were to OCR the PDF to parse, versus digital parsing, then you are dealing with only the number of variants that exist in the invoices themselves.

However crazy it sounds like, the above two are real scenarios and there are many more. I doubt that these problems will always exist, but it makes you think twice about crazy statements such as printing a digital document to image just so you can OCR it.

Chris Riley – About

Find much more about document technologies at www.cvisiontech.com.