In the previous blog, we discussed the theory behind the EAST algorithm. If you remember, we stated that this algorithm is both accurate and efficient. So, in this blog, let’s find it out. For this, first, we will run the EAST algorithm using its Github repository, and then we will analyze the results. So, let’s get started. Here, I’m using a Linux system.
Clone the Repository
First, search “EAST Github” in the browser. You will find several EAST implementations but in this blog, we will use the one provided by argman. So, open this and clone the repository. In order to clone the repository, you can either use git or download it as a zip file. To install git, you can run the following command.
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sudo apt-get install git |
Once you have installed git, clone the repository using the following command.
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git clone https://github.com/argman/EAST.git |
This will clone the repository into your system as shown below.
Compile lanms
As you might remember, in the previous blog, we discussed that the EAST algorithm uses a Locality-Aware NMS (lanms) instead of the standard NMS. Now, you need to compile the lanms. Why? because this GitHub implementation contains the lanms code written in C++ (See the lanms folder). So, in order to make it work with Python, we need to generate an adaptor.so file. This can be done as follows.
First, we need to install the g++ compiler in order to compile the adaptar.cpp file. This can be done using the following command.
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sudo apt-get install build-essential |
This contains the essential tools for building most other packages from source (e.g. C/C++ compiler, libc, and make).
Note: For more details on the Optical Character Recognition , please refer to the Mastering OCR using Deep Learning and OpenCV-Python course.
Next, open the __init__.py file present inside the lanms folder and comment out the if condition as shown below.
Again open the terminal and change the directory to the lanms folder. After this, run the make command as shown below. This will generate the required adaptor.so file in the lanms folder.
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cd EAST/lanms/ make |
Test the model
Now, to test the model, either we need to first train it or find some pre-trained weights, if available. Luckily, pre-trained weights are available. You can download it from here. These are trained on the ICDAR-2013 and ICDAR-2015 datasets.
After downloading the pre-trained weights, extract them and place them inside the EAST folder. Now, to test the model, open the terminal and change the directory to the EAST folder. Also activate the virtual environment if any. Then type the following command by giving the arguments.
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python eval.py --test_data_path=training_samples/ --gpu_list=0 --checkpoint_path=east_icdar2015_resnet_v1_50_rbox/ --output_dir=outputs/ |
For arguments, first, we need to specify the test images path as a “test_data_path” argument. Second, we need to specify the recently downloaded checkpoints path as a “checkpoint_path” argument. And lastly, we need to specify the output directory path as an “output_dir” argument as shown below. This will automatically create the output directory if not present.
This will run the EAST algorithm on the test images we provided. Below an output image is shown.
In the next blog, we will explore different text detection datasets that are available. We will also learn how we can create our own text detection dataset. This will help us with training and fine-tuning our EAST model further. Till then, have a great time. Hope you enjoy reading.
If you have any doubts/suggestions please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.