EasyOCR VS x-transformers

PyTesseract Module [ Github ] EasyOCR Module [ Github ] PaddlePaddle OCR [ Github ]

I just uploaded my C++ implementation of EasyOCR, a well known ocr library for python. Also dusted some cobwebbs from some audio related projects as well, feel free to leave feedback or contribute! I only implemented the most salient parts, so certainly could use some community help! Cheers!

EasyOCR is a popular project if you are in an environment where you can use run Python and PyTorch (https://github.com/JaidedAI/EasyOCR). Other open source projects of note are PaddleOCR (https://github.com/PaddlePaddle/PaddleOCR) and docTR (https://github.com/mindee/doctr).

The main one was https://github.com/JaidedAI/EasyOCR, mostly because, as promised, it was pretty easy to use, and uses pytorch (which I preferred in case I wanted to tweak it). It has been updated since, but at the time it was using CRNN, which is a solid model, especially for the time - it wasn't (academic) SOTA but not far behind that. I'm sure I could've coaxed better performance than I got out of it with some retraining and hyperparameter tuning.

Anyways, you can give a shot to EasyOCR, pretty solid and flexible

The easyocr library: https://github.com/JaidedAI/EasyOCR

Not used it on a Pi but maybe a Docker version (if there is one) would run? Compose file here

it is all documented here, in writing and in code https://github.com/lucidrains/x-transformers

you will want to use rotary embeddings, if you do not need length extrapolation

it is just a single transformer encoder, so just use https://github.com/lucidrains/x-transformers with ff_glu set to True

However, I've come across the concept of Key, Value Caching in Transformer-Decoders recently (e.g. Figure 3 here), wherein because each output (and hence each input, since the model is autoregressive) only depends on previous outputs (inputs), we don't need to re-compute Key and Value vectors for all t < t_i at timestep i of the sequence. My intuition leads me to believe, then, that (unconditioned) inference for a decoder-only model uses an effective sequence length of 1 (the most recently produced token is the only real input that requires computation on), making Attention a linear-complexity operation. This thinking seems to be validated by this github issue, and this paper (2nd paragraph of Introduction).

The Project - Model: The primary architecture consists of a CNN with a transformer encoder and decoder. At first, I used my implementation of self-attention. Still, due to it not converging, I switched to using x-transformer implementation by lucidrains - as it includes improvements from many papers. The objective is simple; the CNN encoder converts images to a high-level representation; feeds them to the transformer encoder for information flow. Finally, a transformer decoder tries to decode the text character-by-character using autoregressive loss. After two weeks of trying around different things, the training did not converge within the first hour - as this is the usual mark I use to validate if a model is learning or not.

X-Transformers: A fully-featured transformer with experimental features\ (25 comments)

One thing I’ve looked at is the fact that there’s no obvious reason to distinguish between W_K and W_Q in the formulation of a transformer as far as I can tell. However if you build a transformer where you merge the two matrices, it doesn’t learn as well. It still learns, but not as well. You can try out the code here. The training loss can be seen here, though we aborted the run because of how poorly it was doing.