Why are RNNs used in some computer vision problems? I am learning computer vision. When I was going through implementations of various computer vision projects, some OCR problems used GRU or LSTM, while some did not. I understand that RNNs are used only in problems where input data is a sequence, like audio or text.
So, in kernels of MNIST on kaggle almost no kernel has used RNNs and almost every repository for OCR on IAM dataset on GitHub has used GRU or LSTMs. Intuitively, written text in an image is a sequence, so RNNs were used. But, so is the written text in MNIST data. So, when exactly is it that RNNs(or GRUs or LSTMs) need to be used in computer vision and when don't?
When I think about RNNs applied to Computer Vision, two main research areas of Deep Learning come up to my mind:

Image Captioning: Neural Networks trained to produce descriptions of images. In that case, you have a Conv ecoder that processed pixel data, and an RNN decoder that produces a description.

Video processing (I don't have a better term). Anything regarding video data requires a mixture of CNNs and RNNs. In general: you process each frame with Conv layers, and the sequence of processed image frames is then fed into some RNN layer that performs the final task, that could be classification, segmentation, self driving cars, ... you name it.