Category: Passport detection python

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Passport detection python

22.04.2021 Passport detection python

Humans can understand the contents of an image simply by looking. We perceive the text on the image as text and can read it. Computers don't work the same way. They need something more concrete, organized in a way they can understand.

Whether it's recognition of car plates from a camera, or hand-written documents that should be converted into a digital copy, this technique is very useful.

passport detection python

While it's not always perfect, it's very convenient and makes it a lot easier and faster for some people to do their jobs. In this article, we will delve into the depth of Optical Character Recognition and its application areas. We will also build a simple script in Python that will help us detect characters from images and expose this through a Flask application for a more convenient interaction medium.

Optical Character Recognition involves the detection of text content on images and translation of the images to encoded text that the computer can easily understand. An image containing text is scanned and analyzed in order to identify the characters in it. Upon identification, the character is converted to machine-encoded text. How is it really achieved? To us, text on an image is easily discernible and we are able to detect characters and read the text, but to a computer, it is all a series of dots.

The image is first scanned and the text and graphics elements are converted into a bitmap, which is essentially a matrix of black and white dots.

The image is then pre-processed where the brightness and contrast are adjusted to enhance the accuracy of the process. The image is now split into zones identifying the areas of interest such as where the images or text are and this helps kickoff the extraction process. The areas containing text can now be broken down further into lines and words and characters and now the software is able to match the characters through comparison and various detection algorithms.

The final result is the text in the image that we're given. The output can now be converted to other mediums such as word documents, PDFs, or even audio content through text-to-speech technologies. Previously, digitization of documents was achieved by manually typing the text on the computer. Through OCR, this process is made easier as the document can be scanned, processed and the text extracted and stored in an editable form such as a word document. If you have a document scanner on your phone, such as Adobe Scan, you have probably encountered OCR technology in use.

Airports can also use OCR to automate the process of passport recognition and extraction of information from them.

Machine readable passport text recognition.

Other uses of OCR include automation of data entry processes, detection, and recognition of car number plates. I chose this because it is completely open-source and being developed and maintained by the giant that is Google.

Follow these instructions to install Tesseract on your machine, since PyTesseract depends on it. We will also use the Flask web framework to create our simple OCR server where we can take pictures via the webcam or upload photos for character recognition purposes. We are also going to use Pipenv since it also handles the virtual-environment setup and requirements management.

Besides those, we'll also use the Pillow library which is a fork of the Python Imaging Library PIL to handle the opening and manipulation of images in many formats in Python. In this post, we'll concentrate on PyTesseract although there are other Python libraries that can help you extract text from images such as:.

Start by installing Pipenv using the following command via Pip In case you need to set it up, refer to this. In case you'll not be using Pipenv, you can always use the Pip and Virtual Environment approach. Follow the official documentation to help you get started with Pip and Virtual Environment :.

Note : In that case, instead of pipenv install Pillowthe command will be pip install Pillow. We are going to implement this project in 2 phases. In the first, we'll create the script, and in the next we'll build a Flask application to act as an interface. With the setup complete, we can now create a simple function that takes an image and returns the text detected in the image - this will be the core of our project:. The function is quite straightforward, in the first 5 lines we import Image from the Pillow library and our PyTesseract library.

Our simple OCR script works!GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

passport detection python

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The datasets have been mentioned in the python notebook. Please refer the python code to get an idea.

Please lookup the keywords mentioned to get more information. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. IRIS detection biometric system using iris images as a dataset. Jupyter Notebook. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit….

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Take a passport photo of precisely the proper dimensions using computer vision to pan and zoom on the face. The program works, but does require you to edit a line by hand at the top if you want to change the image width-to-height ratio.

Shows live camera view. Automatically centers and zooms image to be precisely correct for passport photos. Hit spacebar to snap the photo to "passport.

TIP: If you leave eog passport.

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The face detection routine is a bit CPU hungry so the image is downscaled to fit in a x square before processing to get a better frame rate. At the moment, size is hardcoded to 33mm x 48mm, with the distance from the chin to the bottom of the photo being 7mm. However, it's pretty darn easy to change by editing some numbers at the top of the file. This is annoying, but is easy enough to work around that I'm not fixing it before the release.

How computers learn to recognize objects instantly - Joseph Redmon

Only the first camera is opened. When recentering the face, especially if it is very close to the camera, there may not be enough picture from the camera to extend all the way to the edge of the final photo. The program should warn about this and give a visual indication of which way to move to fix it. If your face is too far away, the camera will zoom in too much, causing a blurry picture. This program should detect if height or width is less than and ask the user to step closer.

Here is a simple Python script for face detection in live video using OpenCV. Short and easy to modify if you want to start another project like this. Skip to content.

passport detection python

Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Take a passport photo of proper dimensions using computer vision. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Customization At the moment, size is hardcoded to 33mm x 48mm, with the distance from the chin to the bottom of the photo being 7mm.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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I am trying to detect the National ID of the below type and get the details of it, For example the location of the signature should be found at the top right corner of the persons image, in this case "BC".

I need to do this application in iphone. I thought of using Opencv for it but how can I achieve the marked details? I also gone through card-io which detects the credit card details, does Card-io detects the other card details also? I have used tesseract for text detection. Tesseract works good if the image has text alone. So I cropped the red marked regions and given as input to Tesseract, it works good with the MRZ part.

Now I am trying to automate the text detection part.

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Now I am planning to automate the following items. Assuming these IDs are prepared according to a standard template having specific widths, heights, offsets, spacing etc. MRZ would be easy to detect. Once you detect it in the image, find the transformation that maps the MRZ in your template to it. When you know this transformation you can map any region on your template for example, the photo of the individual to the image and extract that region.

Below is a very simple program that follows a happy path. You will have to do more processing to locate the MRZ in general for example, if there are perspective distortions or rotations. I prepared the template just by measuring the image, and it won't work for your case. I just wanted to convey the idea. Image was taken from wiki. Result: photo and surname regions in orange. MRZ in blue. There is now the PassportEye library available for this purpose. Learn more. Asked 5 years, 10 months ago.

Active 3 years, 3 months ago. Viewed 28k times.

passport detection python

Is there any specific implementations for mobile applications?It can have 2 lines or 3 lines of machine-readable data. Some applications will need to be able to scan such data of someway, so one of the easiest methods is to recognize it from an image file. You can install this tool on any system unix or windows easily, so check the official repository at Github here. This package provides a kit of tools for recognizing machine readable zones MRZ from scanned identification documents.

The documents may be located rather arbitrarily on the page - the code tries to find anything resembling a MRZ and parse it from there. The recognition procedure may be rather slow - around 10 or more seconds for some documents.

The installation process will take a while. For more information about this library, please visit the official repository at Github here.

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The PassportEye library will expose globally the mrz command, this tool will process a given filename, extracting the MRZ information it finds and printing it out in tabular form. This seems to work fine with most scanner-produced one-page PDFs, but has not been tested extensively.

However the output won't be well formated as the key and value will be separated by a single space, instead you can format it nicely and make it available to be processed by some programming language with the json format. You can generate the output in this format, adding the --json flag to the command:. If you want to integrate this tool within your python code, then you will need to follow a pretty simple logic.

Interested in programming since he was 14 years old, Carlos is the founder and author of most of the articles at Our Code World. Proud Self-taught programmer.What to know what objects are in the image?

Or perhaps you want to count the number of apples in an image? In this post, I will show you how to create your own object detection program using Python in less than 10 lines of code. You will need to install the following python libraries if it is not already installed:. Here is the code to import the required python libraries, read an image from storage, perform object detection on the image and display the image with a bounding box and label about the detected objects.

Below are a few results of object detection using the above code. You have your object detection program ready. Found this post helpful? Leave your thoughts as comments below. Looking to implement face detection. Check out my post on how to implement face detection in less than 3 minutes using python. To know more about cvlib library, you can visit the link below. To know about all the objects that can be detected using this library, you can visit the link below.

Here are some of the further readings for understanding how object detection works:. Sign in. Find out what objects are in the image. Sabina Pokhrel Follow. You will need to install the following python libraries if it is not already installed: opencv-python cvlib matplotlib tensorflow Here is the code to import the required python libraries, read an image from storage, perform object detection on the image and display the image with a bounding box and label about the detected objects.

It was developed with a focus on enabling easy…. You signed in with another tab or window. You signed out in another tab or….

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Here are some of the further readings for understanding how object detection works: Object Detection using Deep Learning Approaches: An End to End Theoretical Perspective A detailed look on the most influential papers in Object Detection. It happens to the best of us and….

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Towards Data Science Follow. A Medium publication sharing concepts, ideas, and codes. See responses More From Medium. More from Towards Data Science. Rhea Moutafis in Towards Data Science. Taylor Brownlow in Towards Data Science. Discover Medium. Make Medium yours. Become a member. About Help Legal.This code used boolean functions to check if all the conditions were satisfied or not.

We see that though the complexity of the code is basic, the length is considerable. Method 2: Using regex. Then we check if the pattern defined by pat is followed by the input string passwd.

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If so, the search method returns truewhich would allow the password to be valid. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute. See your article appearing on the GeeksforGeeks main page and help other Geeks. Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.

Writing code in comment? Please use ide. Conditions for a valid password are: Should have at least one number. Should have at least one uppercase and one lowercase character. Should have at least one special symbol. Should be between 6 to 20 characters long. Input : Geek12 Output : Password is valid.

Input : asd Output : Invalid Password!! We can check if a given string is eligible to be a password or not using multiple ways. Password validation in Python. Function to validate the password. Driver Code. Check out this Author's contributed articles.

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