Tesseract.js is the JavaScript/Webassembly port of the Tesseract OCR engine. We do not edit the underlying Tesseract recognition engine in any way. Therefore, if you encounter bugs caused by the Tesseract engine you may open an issue here for the purposes of raising awareness to other users, but fixing is outside the scope of this repository.
If you encounter a Tesseract bug you would like to see fixed you should confirm the behavior is the same in the main (CLI) version of Tesseract and then open a Git Issue in that repository.
Tesseract.js supports all frameworks that support JavaScript and WebAssembly. The only common JavaScript framework known to not be supported is React Native, as it does not support WebAssembly.
If you are able to run the examples in the examples directory, however are getting a cannot find module error when run in your framework, this indicates the main Tesseract.js thread is unable to find the worker code.
This can be resolved by manually setting the workerPath argument to point to the local copy of worker-script/node/index.js (Node.js) or worker.min.js (browser). For example, the using the following arguments resolved for one Node.js user in this issue. You may need to edit the file paths to work with your system/project.
const worker = await createWorker("eng", 1, {workerPath: "./node_modules/tesseract.js/src/worker-script/node/index.js"});
For context, Tesseract.js "workers" get their own web worker (browser) or worker thread (Node.js), which is independent code that uses a different entry point. When Tesseract.js is used on its own, this entrypoint should be identified automatically. However, this may not hold with build systems implemented by various frameworks, as these build systems copy around files in a way that violates Tesseract.js's assumptions for where files are located.
Tesseract.js does not support .pdf directly—a separate library must be used to convert the .pdf files to images before Tesseract can recognize them. If you are an end user and want to use Tesseract.js to OCR a .pdf file, consider using scribeocr.com, a project that uses Tesseract.js and supports .pdf files. If you are a developer who wants to use Tesseract.js with .pdf files, you can use either of the libraries below to convert from .pdf to images.
Default settings should provide optimal results for most users. If you do want to experiment with configuration settings, Tesseract does include many settings to change—the vast majority are documented in the main Tesseract project and not here. As noted above (“what is the scope of this project”), the core recognition engine is inherited from the main Tesseract project—all of the configuration settings in Tesseract work identically in Tesseract.js. Therefore, for specific questions about configuring recognition settings (e.g. “how can I make noise removal more/less aggressive” or “what settings work best for license plates”) you are more likely to find an answer in the Tesseract documentation/discussion versus only looking in this repo.
No. The Tesseract OCR model is built around assumptions that only hold for printed text. No combination of options will significantly improve performance with handwritten text. Unless your handwriting is so good that it closely resembles printed text, the results will be poor.
During the downloading of language model, Tesseract.js will first check if *.traineddata already exists. (browser: IndexedDB, Node.js: fs, in the folder you execute the command) If the *.traineddata doesn't exist, it will fetch *.traineddata.gz from tessdata, ungzip and store in IndexedDB or fs, you can delete it manually and it will download again for you.
See the documentation from the main Tesseract project for training instructions.