PDFs are everywhere. Reports, invoices, research papers, design proofs, scanned contracts, you are likely to come across them almost every day. And, more frequently than not, those PDFs do have images that are important. Graphs, schemes, photos of products, scanned signatures, medical photographs, have you heard all this before?
Now comes the tricky part. You attempt to extract those pictures and you want clean and high-resolution images. Rather, you get vague images, jagged edges or pictures which do not even resemble the original. Frustrating, right?
It is much more complex to get pictures out of PDFs than it appears to be. Unpacking the reasons why this process is so difficult, and why businesses are hard pressed to do it right, is a good idea.
Not Every PDF is Created the same
The first issue is as follows: PDFs do not have a single design.
There are digitally generated PDFs, which implies that images are placed within the file in a clean manner. Other ones are scanned documents in which the page is a single large image. Then there are hybrid PDFs text layers with image layers. Confusing already?
Using these various formats, the outcome when you are trying to get a visual of it is all over the place. What may suit one PDF and fail miserably in another. Have you ever noticed that the same tool produces different results each time? This is why.
A huge Headache Is Resolution Loss
Most PDFs optimize the images to minimize the size of files. Compression is not properly handled by the extraction tools and the resultant image is not of good quality. Fine details disappear. Written things put in pictures become not readable. And in case the image needs to be printed, analyzed, or complied with? That’s a serious problem.
Embedded vs Rendered Images
This is what the majority of the population is not aware of. There are PDFs which do not contain images.
Charts can be in the form of vector graphics. Shapes could be used to create logos. Graphs can be built up of pathway and line layers. Attempts by tools to retrieve these result in either complete failure to retrieve them or the low-quality raster images.
This is the point where most solution of the simplest kind fail. They are unable to distinguish an embedded image and a rendered visual object. The result? Outputs that are not complete or distorted.
The Scanned PDFs: A Different Beast Altogether
there are also challenged scanned PDFs. Images in these files are not separate items, they belong to a whole page of a scanned image.
Trying to extract images from pdf files like these often means cropping manually or using AI-based detection to identify image regions. The absence of smart processing makes you either barf out the whole page or nothing that is useful.
And what of distorted scanning, shadows, or bad light? Those are the problems that compound the process.
Mixed Content Confusion
There are hardly any business PDFs with a single image. They tend to have tables, logos, stamps, handwritings and photographs, on the same page.
Then how does a system identify what it is going to extract and what it is going to ignore? Should it pull the logo? The signature? The background watermark?
In the absence of context-aware processing, the tools will over-extract (extracting everything) or under-extract (extracting important visuals). Neither option is ideal.
Image Metadata Gets Lost
In case images are not extracted in a good way, some crucial information such as image sizes, color profiles and orientation may be lost. This is a big thing in business sectors such as healthcare, law or design where precision is a concern.
Or have you ever saved a picture and then discovered that it was upside-down, inside-out or strange in some other way? Metadata mishandling at work.
Manual Extraction is not the Solution
However, it would be fair to say that manual extraction does not scale.
It’s slow. It’s inconsistent. And it’s prone to human error. A single image not taken, a single crop made wrong and the entire data set is gone.
The manual work becomes a bottleneck to the teams that have to process hundreds or thousands of PDFs. Automation is not merely useful at this point it becomes necessary.
Capability of Cunning Extraction
Intelligence tools used by modern document represent image extraction in a different manner. They do not blindly pull out elements but study the structure of the document, determine the visual content that has an importance and avoid quality degradation.
This is especially important when businesses need to extract images from pdf files for downstream processes like machine learning, audits, archiving, or customer-facing reports.
Final Thoughts
It would be easy to extract images of PDFs but as you have witnessed, it is not that easy. This is not easy due to different file types, compression problems, scanning documents, mixed contents and loss of metadata.
The good news? These problems can be overcome with intelligent document processing. The businesses are not forced to accept poor images or incomplete views, or manual work-around anymore.
In case images are important to your workflows, which, frankly, they frequently must be, then smarter extraction is not something to skimp on. It’s the only way forward.
Due to the fact that today, in the age of data, clarity is no luxury. It’s a requirement.
