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To overcome this problem people normally use OpenCV to remove noise from the program and then configure the Tesseract OCR engine based on the image to get better results. But the output that we get from pi is “Explain that stuff! Sdfiosiefoewufv” this means that out program fails to recognize any numbers from the image. Finally we print the value of text to check the results.Īs you can see the original image actually contains the text “ Explain that Stuff! 01234567890” which is a perfect test image since we have alphabets, symbols and numbers in the image. Then we use the image_to_sting method from the pytesseract package to detect any text from the image and save it as a string in the variable text. The Pillow package is used to open this image and save it under the variable name img. In the above program we are trying to read text from an image called ‘1.png’ which is located inside the same directory of the program. The above program is given below from PIL import Image img =Image.open (‘1.png’) text = pytesseract.image_to_string(img, config=’’) print (text) The test image I used, the program and the result can be found in the below image.Īs you can see the program is pretty simple and we did not even use any OpenCV packages. We can quickly write a small program to check how the character recognition is working with a test image. Since we have already installed the Tesseract OCR and Pytesseract packages in our PI. Simple Character Recognition Program on Pi
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So we decided to try Tesseract 4.0 on windows and Tesseract 3.04 on Raspberry Pi. Unfortunately through at this time of this tutorial Tesseract 4.0 is only available for Windows and Ubuntu, but is still in beta stage for the Raspberry Pi. Tesseract 4.0 uses Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) to improve the accuracy of its OCR engine. To overcome this, the latest version of tesseract, the Tesseract 4.0 uses a Deep Learning model to recognize characters and even handwritings. If the image has too much background noise or is out of focus tesseract does not seem to work well there. Over the years the Tesseract has evolved, but still it works well only in controlled environments. The Tesseract Optical character recognition project was originally started by Hewlett Packard in 1980 and then was adopted by Google which maintains the project till date. Once the pytesseract installation is complete you window will look something like this
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Other can use that tutorial and install it now. People who have followed the Raspberry Pi face recognition tutorial would have already installed it.
Make sure pillow is already installed before you proceed to this step. Follow the below command to install pytesseract on python. Pytesseract is a python wrapper around the tesseract OCR engine, which helps us to use tesseract with python. Now that we have the Tesseract OCR installed we have to install the PyTesseract package using the pip install package. Your terminal window will look something like below, it will take around 5-10 minutes for the installation to complete.
Then we can proceed with installing the Tesseract OCR (Optical Character Recognition) using the apt-get option. Use the below command on the terminal window to configure Debian Package. To do this we have to first configure the Debian Package (dpkg) which will help us to install the Tesseract OCR. To perform Optical Character Recognition on Raspberry Pi, we have to install the Tesseract OCR engine on Pi.
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Raspberry pi, being a portable and less power consuming device, is used in many real-time image processing applications like Face detection, object tracking, Home security system, Surveillance camera etc. Some of us might have already experienced these features through Google Lens, so today we will build something similar using an Optical Character Recognition (OCR) Tool from Google Tesseract-OCR Engine along with python and OpenCV to identity characters from pictures with a Raspberry Pi. This opens door for many applications like to automatically read the information from a business card, recognize a shop from its name board or recognize sign boards on road and much more. This is called Optical Character Recognition (OCR). In this tutorial we will learn how details are identified from images by reading the characters present on it. Be it a simple food delivery Robot like the Starship Robots or an advanced self driving car like Tesla, they are rely on information obtained from their highly sophisticated cameras to take decisions. The ability of machines to use a camera to look at the real world and interpret data from it would have a greater influence on its applications.
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