What is the accuracy of Tesseract OCR?

images revealed a 71.76\% accuracy for the former and 89.03\% accuracy for the latter. ms processing time, and boosts precision, recall and F1 score from 17\% to 60\%. Tesseract 4.0 from 70.2\% to 92.9\%.

Which of the following image processing techniques can be used to improve the accuracy of Tesseract OCR for recognizing license plates?

Denoising the Image: Gaussian Blur is a technique for denoising images. it makes the edges more clearer and smoother which in-turn makes the characters more readable. Now, pass the transformed license plate file to the Tesseract OCR engine and see the predicted result.

How do I increase my Tesseract speed?

To speed up the process, one should make a list of image paths and feed it to tesseract. Using SSDs or RAM as Disk : If there are large number of images, it can help in saving lot of I/O time. SSDs will have faster access and loading time.

Is Tesseract the best OCR?

Tesseract is the best OCR software open source. Tesseract is actively developed by a community and it is supported by Google (As of June 2019). Recently neural net based OCR engine mode is made available on Tesseract 4.0 which gives improved accuracy for image documents that have high noise (Not well scanned document).

How do I improve image quality in Tesseract?

Three points to improve the readability of the image:

1. Resize the image with variable height and width(multiply 0.5 and 1 and 2 with image height and width).
2. Convert the image to Gray scale format(Black and white).
3. Remove the noise pixels and make more clear(Filter the image).

How can I improve my OCR algorithm?

When it comes to improving OCR accuracy, you basically have two moving parts in the equation.

1. The Quality Of Your Source Image.
2. The OCR Engine.
3. Good Quality Original Source.
4. Scaling To The Right Size.
5. Increase Contrast.
6. Binarize Image.
7. Remove Noise and Scanning Artefacts.
8. Deskew.

How do you train the Tesseract?

Overview of Training Process

1. Prepare training text.
2. Render text to image + box file.
3. Make unicharset file.
4. Make a starter traineddata from the unicharset and optional dictionary data.
5. Run tesseract to process image + box file to make training data set.
6. Run training on training data set.
7. Combine data files.

How do I improve OCR in Python?

Does Tesseract run faster on GPU?

When it comes to speed, Tesseract is more favorable on a CPU machine, but EasyOCR runs extremely fast on a GPU machine. The results are based on white background images with black text, arial font, and 15 font size.

Is Tesseract slow?

It’s working pretty good, but very slow. It takes close to 1000ms (1 second) to read the attached image (00060. jpg) on my quad-core laptop.