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Related tags Machine LearningArtificial IntelligenceData ScienceNeural NetworksAITensorFlowTechnologyComputer VisionSelf Driving Cars Top storiesLatest storiesArchive Adam Geitgey Dec 13 How to break a CAPTCHA system in 15 minutes with Machine Learning Let’s hack the world’s most popular… Read more… 4.8K 15 responses Anson Wong in Towards Data Science Dec 15 Building a Similar Images Finder without any training!
Transfer Learning is… Read more… 1.1K 12 responses Jack Kwok in Insight Data Dec 15 Deep Learning for Disaster Recovery Automatic Detection of Flooded Roads Read more… 265 Sachin Abeywardana in Towards Data Science Oct 1 Sequence to sequence tutorial This is one of the most powerful concepts in deep learning that started off in translation but has since moved on to question answering systems (Siri, Cortana etc.), audio transcribing etc.
How to break a CAPTCHA system in 15 minutes with MachineLearning
How to break a CAPTCHA system in 15 minutes with Machine Learning Let’s hack the world’s most popular Wordpress CAPTCHA Plug-in Everyone hates CAPTCHAs — those annoying images that contain text you have to type in before you can access a website.
Time elapsed so far: 2 minutes Our Toolset Before we go any further, let’s mention the tools that we’ll use to solve this problem: Python 3 Python is a fun programming language with great libraries for machine learning and computer vision.
To break a CAPTCHA system, we want training data that looks like this: Since we have the source code to the WordPress plug-in, we can modify it to save out 10,000 CAPTCHA images along with the expected answer for each image.
After a couple of minutes of hacking on the code and adding a simple ‘for’ loop, I had a folder with training data — 10,000 PNG files with the correct answer for each as the filename: This is the only part where I won’t give you working example code.
Time elapsed so far: 5 minutes Simplifying the Problem Now that we have our training data, we could use it directly to train a neural network: With enough training data, this approach might even work — but we can make the problem a lot simpler to solve.
And we can’t just split the images into four equal-size chunks because the CAPTCHA randomly places the letters in different horizontal locations to prevent that: The letters in each image are randomly placed to make it a little more difficult to split apart the image.
So we’ll start with a raw CAPTCHA image: And then we’ll convert the image into pure black and white (this is called thresholding) so that it will be easy to find the continuous regions: Next, we’ll use OpenCV’s findContours() function to detect the separate parts of the image that contain continuous blobs of pixels of the same color: Then it’s just a simple matter of saving each region out as a separate image file.
Sometimes the CAPTCHAs have overlapping letters like this: That means that we’ll end up extracting regions that mash together two letters as one region: If we don’t handle this problem, we’ll end up creating bad training data.
In that case, we can just split the conjoined letter in half down the middle and treat it as two separate letters: We’ll split any regions that are much wider than they are tall in half and treat it as two letters.
We’ll use a simple convolutional neural network architecture with two convolutional layers and two fully-connected layers: If you want to know more about how convolutional neural networks work and why they are ideal for image recognition, check out Adrian’s book or my previous article.
Time elapsed: 15 minutes (whew!) Using the Trained Model to Solve CAPTCHAs Now that we have a trained neural network, using it to break a real CAPTCHA is pretty simple: Grab a real CAPTCHA image from a website that uses this WordPress plugin.
🔥 Latest Deep Learning OCR with Keras and Supervisely in 15minutes
This tutorial is a gentle introduction to building modern text recognition system using deep learning in 15 minutes.
This guide is for anyone who is interested in using Deep Learning for text recognition in images but has no idea where to start.
Through long research and reading many papers we have developed an understanding of main principles behind creating effective recognition systems.
And we have shared our understanding with community in two small video lectures (part1 and part2) and explain how it works in plain language.
We feel that this content is extremely valuable, because it is impossible to find nice and simple explanation of how to build modern recognition systems.
We at DeepSystems do a lot of computer vision developments like self-driving car, receipt recognition system, road defect detection and so on.
We as data scientists spend a lot of time to working with training data: creating custom image annotations, merging our data with public datasets, making data augmentations and so on.
Random 95 percent of images will be added to “train” set, and the rest 5 percent to “val” set.
Click “Export” -> “Task status” -> “Three vertical dots” -> “Download” button to get training data (marked in red).
Clone it with the following commands git clone https://github.com/DeepSystems/supervisely-tutorials.git cd supervisely-tutorials/anpr_ocr Directory structure will be the following .
└── image_ocr.ipynb Put downloaded zip archive into “data” directory and run the command below unzip <archive name="">.zip -d .
Run next command to start Jupyther notebook jupyter notebook In terminal you will see something like this You have to copy selected link and paste it into web browser.
Notebook consists of few main parts: data loading and visualisation, model training, model evaluation on test set.
Please, don’t be lazy and take 15 minutes to watch our small and simple video lecture about high level overview of NN architecture, that was mentioned at the beginning.
Image has the following shape: height equals to 64, width equals to 128 and num channels equal to three.
As you have seen before we feed this image tensor to CNN feature extractor and it produces tensor with shape 4*8*4.
Height equals to 4, width equals to 8 (These are spatial dimentions) and num channels equals to 4.
In practice number of channels should be much larger, but we constructed small demo network only because everything fit on the slide.
Then we apply fully connected layer followed by softmax layer and get the vector of 6 elements.
We added blank symbol to the alphabet to teach our neural network to predict blank between such case symbols.
We believe that video lectures, this tutorial, ready-to-use artificial data and source code will help you get basic intuition and that everyone can build modern OCR system from scratch. </archive>
Rocket AI: 2016’s Most Notorious AI Launch and the Problem with AIHype
It’s now common for savvy AI researchers to create a company purely to be acquired, knowing that the right buzzwords will attract VCs, and that eventually a corporation will pay for the team.
In reality, deep learning, reinforcement learning and other branches of machine learning are decades-old ideas, brought to life in recent times by the availability of massive data sets.
Open AI’s launch of Universe gave the AI community thousands of game and web environments in which to train their algorithms, a luxury previously available only to large players like Google, who have internal teams creating simulated environments.
Investors aren’t involved enough in the space to know that the first Reinforcement Learning textbook was written in 1998, or that what separates a successful applied machine learning company is often the novelty, quality and/or quantity of the data they have access to.
The danger of hype is that it diverts attention away from researchers who build the tools and theories that advance the field, or the real companies trying to solve problems with machine learning optimization.
Having watched the field change the last few years, the AI community knew exactly what to do to play hype at its own game and help support the launch of the ultimate fake AI company.
Unless more of us fight for technical integrity and try to educate more capital allocators, then someday soon the bubble will burst and real scientific progress and real companies will lose.
- On 20. januar 2021
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