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https://github.com/robertknight/ocrs
Rust library and CLI tool for OCR (extracting text from images)
https://github.com/robertknight/ocrs
computer-vision machine-learning ocr
Last synced: about 2 months ago
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Rust library and CLI tool for OCR (extracting text from images)
- Host: GitHub
- URL: https://github.com/robertknight/ocrs
- Owner: robertknight
- License: apache-2.0
- Created: 2024-01-02T09:32:23.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-04-12T08:06:43.000Z (5 months ago)
- Last Synced: 2024-04-14T13:53:05.644Z (5 months ago)
- Topics: computer-vision, machine-learning, ocr
- Language: Rust
- Homepage:
- Size: 796 KB
- Stars: 849
- Watchers: 5
- Forks: 26
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE-APACHE.txt
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README
# Ocrs
**ocrs** is a Rust library and CLI tool for extracting text from images, also known as OCR (Optical Character Recognition).
The goal is to create a modern OCR engine that:
- Works well on a wide variety of images (scanned documents, photos containing
text, screenshots etc.) with zero or much less preprocessing effort compared
to earlier engines like [Tesseract][tesseract]. This is achieved by using
machine learning more extensively in the pipeline.
- Is easy to compile and run across a variety of platforms, including
WebAssembly
- Is trained on open and liberally licensed datasets
- Has a codebase that is easy to understand and modify
Under the hood, the library uses neural network models trained in
[PyTorch][pytorch], which are then exported to [ONNX][onnx] and executed using
the [RTen][rten] engine. See the [models](#models-and-datasets) section for
more details.
[onnx]: https://onnx.ai
[pytorch]: https://pytorch.org
[rten]: https://github.com/robertknight/rten
[tesseract]: https://github.com/tesseract-ocr/tesseract
## Status
ocrs is currently in an early preview. Expect more errors than commercial OCR
engines.
## Language Support
ocrs currently recognizes the Latin alphabet only (eg. English). Support for
more languages is [planned](https://github.com/robertknight/ocrs/issues/8).
## CLI installation
To install the CLI tool, you will first need Rust and Cargo installed. Then
run:
```sh
$ cargo install ocrs-cli
```
## CLI usage
To extract text from an image, run:
```sh
$ ocrs image.png
```
When the tool is run for the first time, it will download the required models
automatically and store them in `~/.cache/ocrs`.
### Additional examples
Extract text from an image and write to `content.txt`:
```sh
$ ocrs image.png -o content.txt
```
Extract text and layout information from the image in JSON format:
```sh
$ ocrs image.png --json -o content.json
```
Annotate an image to show the location of detected words and lines:
```sh
$ ocrs image.png --png -o annotated.png
````
## Library usage
See the [ocrs crate README](ocrs/) for details on how to use ocrs as a Rust
library.
## Models and datasets
ocrs uses neural network models written in PyTorch. See the
[ocrs-models](https://github.com/robertknight/ocrs-models) repository for more
details about the models and datasets, as well as tools for training custom
models. These models are also available in ONNX format for use with other
machine learning runtimes.
## Development
To build and run the ocrs library and CLI tool locally you will need a recent
stable Rust version installed. Then run:
```sh
git clone https://github.com/robertknight/ocrs.git
cd ocrs
cargo run -p ocrs-cli -r -- image.png
```
### Testing
Ocrs has unit tests for the code that runs before and after ML model processing,
plus E2E tests which exercise the whole pipeline, including models.
After making changes to the code, run unit tests and lint checks with:
```sh
make check
```
You can also run standard commands like `cargo test` directly.
Run the E2E tests with:
```sh
make test-e2e
```
For details of how the ML models are evaluated, see the
[ocrs-models](https://github.com/robertknight/ocrs-models) repository.