naps2 VS doctr

If you have a HP Printer there is scanning software program (Free and Open source) called Not Another PDF Scanner. https://www.naps2.com/ It's simple to use and works really good with HP printers unlike the software that comes with the printers. Everyone who wants to scan with a HP printers needs this software.

Brother DCP-L2550DW here. One of the cheapest b/w multifunction devices with automatic document feeder and reasonable print and scan performance. Works like a charm on Linux, Windows, Android, and IOS.

I am using it with [NAPS2](https://www.naps2.com/), which is brilliantly simple, multi-platform, free, and open-source.

If you want to add page, remove page, split, merge, reorder, re-orient all or individual pages, ... May I recommend the glorious NAPS2 [1] ?

It's meant as a scanning tool but works just fine without scanning just drag and drop a pdf on it.

It doesn't do in-page editing or annotation, it's "one layer above" that.

[1] https://www.naps2.com/

Tips: For PDF management/splitting/rotation/cropping ect before import: NAPS2 is a good tool (Windows, Linux, Mac). It also support ocr (same as paperless-ngx use) https://www.naps2.com/

To scan to image files, use the included Windows Fax & Scan app. To scan to PDF files, use NAPS2.

The software I use is: https://www.naps2.com/

If you are on windows this can scan and OCR using tesseract. https://www.naps2.com/

Try https://www.naps2.com/ It has Arabic support. Once installed with OCR settings, if you drap pdf onto app, it does OCR.

https://github.com/mindee/doctr/issues/1049

I am looking for something this polished and reliable for handwriting, does anyone have any pointers? I want to integrate it in a workflow with my eink tablet I take notes on. A few years ago, I tried various models, but they performed poorly (around 80% accuracy) on my handwriting, which I can read almost 90% of the time.

Yup! But I'm still exploring options. (any recommendations would be welcomed!) Here are some candidates I'm considering:

- https://github.com/mindee/doctr

- https://github.com/open-mmlab/mmocr

- https://github.com/PaddlePaddle/PaddleOCR (honestly I don't know Mandarin so I'm a bit stuck)

- https://github.com/clovaai/donut - While it's primarily an "OCR-free document understanding transformer," I think it's worth experimenting with. Think I can sort this out by letting the LLM reason through it multiple times (although this will impact performance)

- yesterday got a suggestion to consider https://github.com/kakaobrain/pororo - I don't think development is still active but the results are pretty great on Korean text

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).

Last I checked I saw a grocery bill example using https://github.com/mindee/doctr and was fairly accurate. Bear in mind that was last year, hopefully it got even better or there are other libraries

For its OCR service, Cape uses the excellent Python docTR library. Some of the critical benefits of docTR are its ease of use, flexibility, and matching state-of-the-art performance. The OCR model consists of two steps: text detection and text recognition. Cape uses a pre-trained DB Resnet50 architecture for detection, and for recognition, it uses a MobileNetV3 Small architecture. To learn more about the level of OCR accuracy you can expect for your document, you can consult these benchmarks provided by docTR. As you will see, model performance is very competitive compared to other commercial services.

There's also DocTR which can do text detection and extraction out of the box.

It's command line driven but can display the detected text as an overlay of the document.

https://github.com/mindee/doctr

If you want to OCR a document image, modern versions of Tesseract can work well. If you last used it a few years ago, the recognition has improved since due to a new text recognition algorithm that uses modern (deep learning) techniques. Browser demo using a modern version: https://robertknight.github.io/tesseract-wasm/.

OCR processing typically consist of two major steps: detecting/locating words or lines of text on the page, and recognizing lines of text.

Tesseract's text recognition uses modern methods, but the text detection phase is still based on classical methods involving a lot of heuristics, and you may need to experiment with various configuration variables to get the best results. As a result it can fail to detect text if you present it with something other than a reasonably clean document image.

Doctr (https://github.com/mindee/doctr) is a new package that uses modern methods for both text detection and recognition. It is pretty new however and I expect will take more time and effort to mature.