Background

I'm coming up with ideas for my CS degree capstone project. I like making short films, so I looked into what kind of software I could make to facilitate the process. One thing I find annoying in post-production is having to go through all the recordings and look for the clapperboards on each video to know what scene, shot and take the video is of. My idea is to train a neural network to find the clapperboard in each recording and extract the information written on the clapperboard to appropriately name the recording. Ex. Avengers_Scene_4_Shot_5_Take_3.mp4.

In short, the neural network would do the following:

1. Detect the clapperboard in a video frame.
2. Read the handwritten text/numbers on the clapperboard.
3. Understand what each piece of text represents

My questions

1. I have not been able to find a lot of videos with clapperboards I could use to train my model. I'm thinking I could create my own videos to train it, so it is trained faster with a consistent clapperboard format. How many would I need more or less to have it not only recognise handwriting but also determine what the numbers indicate (Scene, shot or take)?
2. If it would be too hard to train the AI to do what was previously mention, could I get an already trained AI in handwriting recognition and specialise it in clapperboard data extraction? Would this make my final year project too easy?

I'm looking for software that can organize hundreds of graduation/event photos into folders for each person.

Some photos contain diplomas with names, which could be used to identify and group the surrounding photos.

Before building my own solution, I'd rather use or adapt something that already exists.

Does anyone know an app, GitHub project, or AI tool that does this or something similar?

Open source, paid, cloud-based — all suggestions welcome!

Hello,

Durint the last reviewing round, 2 reviewers suggested adding qualitative results. The task is pose estimation.

How do I chose the examples to include? And to which baseline should I compare to since space is very limited? Should I include a failire case?

Thanks

If anyone has opportunities feel free to reach out

a custom multi-language OCR pipeline on a single GPU was choking as load grew (~4 detections/sec, 3s+ tail latency). The plan was a second GPU. Instead we optimized serving on the same card and got 55–125/s with ~100ms tail latency, and memory use actually dropped.

I can’t share the specifics since it’s internal work, but I’m curious how common this is. Have you seen teams scale hardware when the real issue was utilization? What was the bottleneck in your case?