ANPR Project Using the Intel Edison

Introduction: ANPR Project Using the Intel Edison

This is an Automatic Number Plate Recogniton Project using openCV and the Intel Edison board.

An Overview:

Capture an Image,pre-process it in opencv then pass it through tesseract OCR to get the characters in the image.Then display the characters on the grove LCD screen.


1.Installing OpenCV on Edison
2.Installing Tesseract-OCR on Edison(the optical character recognition engine we're using)

3.Pre-processing plate image on OpenCV

4.Passing Plate image to tesseract OCR engine to obtain characters.

5.Display the plate characters on an LCD using libmraa

Step 1: Installing OpenCV on Edison

Install the latest IoT Developer Kit Libraries

echo "src intel-iotdk" > /etc/opkg/intel-iotdk.conf

Update the package repository, then upgrade all the packages:

opkg update
opkg upgrade

Add unofficial repository

Access to every package is not available without adding repository locations to the opkg/base-feeds.conf file. By doing this, you’ll add an enormous number of compiled applications, saving you the hassle of compiling from source.

vi /etc/opkg/base-feeds.conf

and paste in

src/gz all  edison  core2-32 

Then hit your Escape key and type :wq to save and exit from vi

Update the repository index again, since you just added new package locations: opkg update Next, install NumPy, OpenCV, and OpenCV-Python.

opkg install python-numpy opencv python-opencv nano

Step 2: Installing Tesseract OCR on the Edison:

Install python setup tools to get pip,easy_install et al

opkg  install python-setup tools

Install PIL(Python Imaging Library)

pip install pil

Install tesseract-OCR and imagemagick

opkg install tesseract-ocr imagemagick

imagemagick is needed for converting the files from .jpg to .tiff format that tesseract can process.

Install pytesser

Step 3: Processing the Image

Now in your python file you will just be required to import the required files from PIL and tesseract and you are SET

from PIL import Image
 from pytesser.pytesser import *

Pre-Process the image using OPENCV algorithms to make it suitable for tesseract OCR:

A. Resize image so that it doesn't take too much processing power:

original = cv.LoadImageM("plate.jpg") resize = cv.CreateMat(original.rows/ 10, original.cols / 10, original.type) cv.Resize(original, resize)

B. Convert to GRAYSCALE


C.Apply threshold I.e binarize the image resized

cvThreshold(image, binary_image,128,255,

the above step returns a binary image which is adaptively thresholded. The arguments follow the order:1. Source image, 2. Destination image, 3. Threshold value,4. Resultant value, and 5. Type of threshold. Play around with the threshold and resultant values till you get the right fit.

Step 4: Recognize Characters Using Tesseract Engine:

Pass the image processed from opencv to your tesseract method.

You will have to convert it to a .tiff file first..this is done using PIL:

from PIL import Image img ='image.jpeg')'image.tiff')

Then pass it through the tesseract ocr engine which will recognoze images and return the characters:

from PIL import Image
from pytesser.pytesser import *
img ='plate.jpeg')'image.tiff')
im =
text = image_to_string(im)
text = image_file_to_string(image_file)
text = image_file_to_string(image_file, graceful_errors=True)
print "=====output=======\n"print text

From there on use the libmraa libraries to print the plate character to an LCD screen..Refer to this link to set up the Grove LCD to display the plate characters.

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    4 Discussions


    3 years ago

    For me tesseract-ocr is not installing on intel edison. How to solve this???


    5 years ago on Step 1

    Hey there! Nice Instructable! I wanted to know whether I actually need python for this. I use OpenCV with C++. Is it enough if I install OpenCV and a C++ Compiler?

    David Kana
    David Kana

    5 years ago on Introduction


    I have tried the steps. but i am having trouble with the tesseract-ocr (segmentation fault and i don't know why opkg was not able to find imagemagick