Text Recognition for PDFs: How Does It Work?
In this article, we start with the basics: How do I enable OCR in PDF? How can I tell if a PDF is text searchable? How do I make text searchable in Adobe PDF? And most of all, how can I extract the key information of that PDF to train a Machine Learning model? Let's dive into it.
What is PDF Text Recognition?
PDF text recognition can automatically recognize and extract text from a PDF document and present it in a readily available text format. Rather than having to manually transcribe a PDF document, instead OCR (optical character recognition) technology is used to automatically identify text elements and convert them into usable text that can be searched and copied. Read on to learn how this technology works and why it is useful.
How does PDF Text Recognition Work?
PDF text recognition utilizes OCR technology to identify elements (images, graphs) & text characters in a scanned document. OCR works by analyzing the patterns of light and dark pixels that make up the defined characteristics of each character and then applies these patterns to known rulesets in order to identify each individual character in a document. As a result, you get actionable data from an otherwise non usable raw format.
In the early days of OCR, this technology was quite primitive and required the use of a special font set in order to work, but modern OCR technology is no longer limited to this and is even capable of recognizing handwriting in addition to digital font sets.
What are the Challenges of Converting PDF to Text?
When documents are scanned or otherwise converted into searchable PDFs, there are numerous challenges that must be overcome to turn the original files into data that can be used to train a Machine Learning model. One of the most immediate issues is that there is no universally consistent scanned document - a book, a legal document, a poster, and images of text may contain writing in many different forms, shapes, and sizes. AnOCR tool must be able to recognize text however it is present on a page.
The original document may be in a high resolution or a low resolution and it may come in virtually any language. Higher resolution documents are always easier to scan, but in any case, being able to recognize text means being able to recognize not only the Latin alphabet but also the many other writing systems in use around the world. One of the challenges of OCR that must be solved is to be able to recognize all of these characters, shapes, and symbols in varying degrees of fidelity.
Discover how training data can make or break your AI projects, and how to implement the Data Centric AI philosophy in your ML projects.
How are these Challenges Solved?
Once a document has been scanned into a program such as Adobe Acrobat, the OCR software must perform a number of functions in order to successfully turn a document into searchable text. One challenge that must be overcome is to identify text when it has been rotated - for example, if care has not been taken, scanned documents can often end up being scanned at an angle. The method for dealing with this is to simply digitally rotate the document until the characters match with known patterns. Since documents are usually (but not always) written using a consistent horizontal baseline, once the angle of the characters is known, that rotation can be accounted for and text can be recognized as if it were on a straight line.
The challenge of identifying characters in a variety of writing systems used to be slightly more complex when OCR relied on using specific font sets, but with the advent of Unicode, it too has become rather trivial. The goal of Unicode is to represent and provide for the encoding of as many characters of as many writing systems as possible. This has transformed foreign language recognition into exactly the same process of character pattern recognition as the language locale of your particular device. Whether the document is written using Latin, Cyrillic, or Hangeul characters, the same basic principles apply.