Kornia
pytesseract
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Kornia | pytesseract | |
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11 | 11 | |
9,227 | 5,457 | |
1.6% | - | |
9.4 | 7.7 | |
4 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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Kornia
- [P] Kornia: Differential Computer Vision
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Hacker News top posts: May 10, 2022
Kornia: Differential Computer Vision\ (3 comments)
- Kornia: Differential Computer Vision
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Top 5 Python libraries for Computer vision
Kornia - Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.
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[P] Using PyTorch + NumPy? A bug that plagues thousands of open-source ML projects.
Use kornia.augmentation where this problem is solved doing the augmentations in batch outside the dataloader. https://github.com/kornia/kornia
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SpaCy v3.0 Released (Python Natural Language Processing)
I haven't had a situation to use it, but I think Kornia looks cool: https://github.com/kornia/kornia
pytesseract
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What's the BEST way to detect these letters on an image?
If you don't have it already: https://github.com/madmaze/pytesseract
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....
As far as working with reading text from a image there are lots of different libraries for doing this sort of thing, but one of the biggest is probably pytesseract. It is extremely powerful for image to text, and reliably beats alphabet soup captchas.
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Extract Highlighted Text from a Book using Python
I'm going to use the Tesseract OCR engine and library, and its Python wrapper PyTesseract for text extraction. But there are numerous libraries out there to extract text from an image. In a real world application I would probably use cloud services from AWS, Google or Microsoft to handle this task.
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exporting handwritten dataset as text, export it and use it as a csv
Yeah, I’m pretty sure the Remarkable OCR is not up to these kinds of tasks unfortunately. If you know some coding you could write something that’d likely work well in Python using for ex. this for receiving the mail attachment and this for converting the PDF to CSV. This is in case you’d write your data as a table on the Remarkable, which I guess is preferable to writing something like (0.5, 8.4, -0.3). If you’d rather do it that way, there are other more suitable OCR tools like this one. The checkbox use-case in the comment above would also be possible by modifying this approach. DM if you’d like to discuss further work.
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Top 5 Python libraries for Computer vision
pytesseract - Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded in images. Python-tesseract is a wrapper for Google's Tesseract-OCR Engine. It is also useful as a stand-alone invocation script to tesseract, as it can read all image types supported by the Pillow and Leptonica imaging libraries, including jpeg, png, gif, bmp, tiff, and others. Additionally, if used as a script, Python-tesseract will print the recognized text instead of writing it to a file.
What are some alternatives?
pyocr
tesseract-ocr - Tesseract Open Source OCR Engine (main repository)
OpenCV - Open Source Computer Vision Library
tesserocr - A Python wrapper for the tesseract-ocr API
Signalum - To explore creating an application that detects available connections at once from wifi and bluetooth
Face Recognition - The world's simplest facial recognition api for Python and the command line
normcap - OCR powered screen-capture tool to capture information instead of images
Camelot - A Python library to extract tabular data from PDFs
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
SimpleCV - The Open Source Framework for Machine Vision
multi-object-tracker - Multi-object trackers in Python