Automatic document recognition is an integral part of modern business processes. But what happens when documents aren’t available as cleanly scanned PDFs, but as photos — taken with a smartphone, often under poor conditions?
This is exactly where the challenges of document recognition begin — challenges that we solve every day in our projects using PRISM.
Last updated: July 3, 2025
In this article, we’ll show you the nine steps of our processing pipeline with PRISM, which allows us to optimize even difficult photo captures so that we can recognize documents automatically — and we’ll take a closer look at the biggest challenges along the way.
9 steps for successful document recognition from photo captures
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Black-and-white conversion
Color tones are removed to improve text recognition – unless colored markings are relevant in the project. -
Brightness and contrast correction
We precisely adjust brightness and contrast so that the text stands out clearly from the background. -
Sharpening
Blurry edges are selectively sharpened to increase recognition quality. -
Correction of alignment
Documents captured at an angle are realigned so that text lines can be reliably recognized. -
Removal of noise artifacts
Disruptive artifacts caused by camera or compression processes are reduced using AI.
The biggest document recognition challenges in detail
6. Depth of field correction
When a document is photographed from a certain angle, only part of it may be in the camera’s focus.
The example to the right is, of course, an extreme case (you’d be surprised at the photos we’ve already seen), but it illustrates the problem well:
In this shot the depth of field is very shallow, and therefore only the front or rear part could be captured in focus. The front (green) was chosen – making the rear (red) unreadable.
In the example shown, it becomes very difficult to extract any information here. However, in less exaggerated cases, our AI manages very well to correctly process only the affected part of the shot. A smooth correction with increasing sharpness filters must be applied here. The bottom line is that this shooting error can be counteracted so well.
7. Perspective correction
In a similar situation as before, the angle may be very poorly chosen, but the content is at least sharp (or sharpened by step 6).
Nevertheless, text recognition then faces a completely different hurdle with such a photo: The text converges to the right in a trapezoidal shape; the font is much larger on the left side than at the end of each line.
Again, we have trained an AI to initiate appropriate geometric countermeasures. Thus, the font lines that converge radially regain their order, and the document as a whole can be reshaped correctly based on the detected line geometry.
8. Correction of distorted areas
The challenge of point 7 has an increase: in the picture on the right you see that the paper hangs from a table edge at a certain point. As a result, a bend starts there – and aggravating it again can be the depth of field.
Targeted training of the AI on such special cases gave us surprisingly reliable results. Especially the recognition of text lines plays its big advantage here: For a well-trained AI, fixing the unusual deformation is a snap. A challenge that would still be a Sisyphean task with conventional, logical programming shows quite clearly the advantages of self-learning AI networks.
9. Wrinkling correction (Dewarping)
Some customers seem to carry their documents around in their pockets before submitting them for further processing. Photographed documents like the one pictured on the right do indeed occur. And even if they first appear to be an insurmountable obstacle for text recognition, we can reassure you: It works!
In fact, it works so well that we ourselves were quite surprised by the results of our AI.
Conclusion: Mastering document recognition from photo captures
Document recognition from photo captures is a complex field with many pitfalls. But with the right AI-powered methods, we can optimize even the most difficult captures so that we can automatically recognize documents—reliably and precisely.
If you are also facing similar document recognition challenges, get in touch with us — we’ll be happy to support you!