This happens all the time in real post-production. A client reviews proofs in Pixieset, Pic-Time, ShootProof, or another gallery platform, then sends back screenshots of the filenames they want edited. The intention is clear, but the format is messy. Instead of a clean export or folder of selected JPEGs, you get a few mobile screenshots and a message saying, "These are the ones we want."
The problem is that screenshots are not directly usable inside your RAW workflow. You still have to extract the filenames, find the corresponding RAW files, and organize them for final editing.
Why screenshot selections slow everything down
Screenshots add friction because they interrupt the normal proof-to-edit handoff. Instead of selecting files or sending a copied list, the client sends visual evidence of the picks. That means the photographer has to do extra translation work before any real post-production begins.
Usually the process turns into:
- Zoom in on each screenshot.
- Read or manually retype the visible filenames.
- Search for those basenames inside the original RAW folders.
- Hope nothing gets mistyped or missed.
It is not difficult in theory. It is just tedious, easy to mess up, and frustrating when you are already dealing with large shoots and long editing queues.
A cleaner workflow for gallery screenshots
The best approach is to split the task into two simple stages. First, convert the screenshot into filename text. Second, match that filename list against your source RAW files.
In practice, the workflow looks like this:
- Ask the client screenshot to show filenames as clearly as possible.
- Crop tightly around the filename list if needed.
- Extract the visible names into text.
- Use those names to match the corresponding RAW files by basename.
- Export only the matched RAW files for editing.
Once the filenames are in text form, the rest of the workflow becomes much more normal. You are no longer hunting one image at a time through nested folders. You are simply matching a known list against your RAW pool.
Why OCR matters here
OCR is useful in this exact scenario because it removes the most annoying part of the process: manual retyping. If the screenshot is reasonably clear and tightly cropped, filename extraction can be fast enough to keep your workflow moving.
You still want a quick human review after extraction, especially if the screenshot is small, compressed, or cluttered. But even with that review step, OCR is usually much faster than re-entering every filename by hand.
Where RAW Matcher fits
RAW Matcher is designed for exactly this kind of handoff. You can bring in selected JPEG names from files, pasted text, or screenshot-based OCR, then choose your RAW source files and match them in the browser. That makes it useful when clients send inconvenient but common selection formats, including screenshots from proof galleries.
In other words, the app does not just help when clients send perfectly organized picks. It also helps when the real-world handoff is messy and you still need to get to the right RAW files quickly.
A practical takeaway
If you often receive screenshot selections, the goal is not to force clients into a perfect system every time. The goal is to have a reliable process for turning those screenshots into a clean list of filenames, then into the corresponding RAW files for final edit.
That saves time, reduces missed files, and keeps post-production moving even when the client handoff is less than ideal.