OCR Model Comparison
There are several OCR (Optical Character Recognition) models available that can be used for text extraction from images. In this article, we will compare some popular OCR models:
- Tesseract OCR: Tesseract OCR is an open-source OCR engine developed by Google. It is widely used for text recognition in various applications.
- EasyOCR: EasyOCR is a lightweight OCR engine that is easy to use and provides high accuracy in text recognition.
- Keras-OCR: Keras-OCR is a deep learning-based OCR model that is built using the Keras library. It is popular for its easy integration with other deep learning frameworks.
- Paddle OCR: Paddle OCR is an OCR model developed by Baidu that provides high-speed and accurate text recognition.
- MMOCR: MMOCR is a multi-modal OCR model that can handle multiple languages and different types of text layouts.
- OCR-SAM: OCR-SAM is a spatial attention model for OCR that focuses on improving the accuracy of text extraction in complex image backgrounds.
Each of these OCR models has its own strengths and weaknesses, and the best model for a particular use case depends on factors such as the type of images being processed, the required accuracy, and the speed of the OCR engine.
It is important to evaluate and compare these OCR models based on performance metrics such as accuracy, speed, and ease of integration before choosing the right model for your project.
Overall, Tesseract OCR, EasyOCR, Keras-OCR, Paddle OCR, MMOCR, and OCR-SAM are all popular choices for text extraction from images, and each has its own unique features that make it suitable for different applications.
Could you please provide source code
could you please provide this colab notebook
Incredibly high-quality overview! Thank you!
Hi could you please help us with our project?
We want to detect all text on screenshot of software. This is a more complex task than we tough, Tesseract and EasyOCR doesn't work.
We did a lot of preprocessing (binarization, denoise, scale*3, grayscale…) and managed to go up to 90% detection on tesseract on most software.
Our software is an AI that answer question based on what it saw on the screen ( It is called onistep ). This is a desktop app. This will be used by all our client on their end so we cannot use the GPU and the answer must take 5 second maximum.
We think that solution like MMocr, keras OCR or paddle OCR could work but we never worked with machine learning based OCR.
Do you think you can work with us on this? Do you think it is doable? Of course it would be paid work.
I also sent you an email with some image preprocessed if you wanna test our image.