binaryen VS EasyOCR

The Binaryen wasm optimizer (mentioned in the article) is always open for contributions,

https://github.com/WebAssembly/binaryen

The end of the related work section cites both wasm-smith and the Binaryen fuzzer (https://github.com/WebAssembly/binaryen/wiki/Fuzzing) and says, "They both provide a fuzzer that turns a stream of bytes into a WebAssembly module in order to test implementations. Their fuzzers always generate semantically valid test cases, but lack the targeting and tuning that Xsmith provides."

I look forward to reading more about how they do the targeting and tuning.

Binaryen or the LLVM of wasm: https://github.com/WebAssembly/binaryen

Download Binaryen

Probably https://github.com/WebAssembly/binaryen/, there were various rust bindings to it.

The final phase of our compiler is code generation. This phase takes the AST and converts it to a set of executable instructions. In our case, WebAssembly. To accomplish this, we are going to use a popular WebAssembly compiler toolchain called binaryen.

In this guide, we will be using TypeScript and NodeJS. The concepts are highly portable, so feel free to use the environment you're most comfortable with. Our only major dependency, binaryen, has a simple C API. You are welcome to skip ahead to the next section if you're using a different language.

What are the size and performance benefits of processing the Wasm payload with wasm-opt?

I would recommend looking into binaryen as it has it's own IR and can perform optimizations over it. It's also simpler than LLVM and has the option to produce binaries with debug names.

You can also use binaryen to optimize your wasm output

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