Have you ever wished to not just read, but understand complex patterns in images? Enter the world of Convolutional Recurrent Neural Networks (CRNN), an innovative blend of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This blog will guide you step-by-step in leveraging CRNN for image-based sequence recognition tasks, such as scene text recognition and Optical Character Recognition (OCR).

What is CRNN?

CRNNs combine the feature extraction capabilities of CNNs with the sequence modeling capabilities of RNNs, allowing them to recognize patterns in data that are not only spatial but also temporal in nature. If CNNs are like skilled artists analyzing a painting, breaking it down into strokes and colors, RNNs act as attentive listeners, interpreting a narrative by remembering previous parts of the story.

Getting Started

To implement CRNN, you need a suitable environment that meets certain requirements.

• Operating System: Tested on Ubuntu 14.04 (x64).
• GPU: A CUDA-enabled GPU is required.

Building the CRNN Software

Follow these steps to build your CRNN project:

1. Install the latest versions of Torch7, fblualib, and LMDB:
• On Ubuntu, install LMDB by running: apt-get install liblmdb-dev.
2. Navigate to the source directory: cd src.
3. Execute the build script: sh build_cpp.sh.

If successful, you will find a file named libcrnn.so in the src directory.

Running the Demo

Before running the demo, follow these steps:

1. Download a pretrained model from here.
2. Place the downloaded model file crnn_demo_model.t7 into the directory model/crnn_demo.
3. Launch the demo with the command: th demo.lua.

The demo program reads an example image and recognizes its text content!

Using the Pretrained Model

With the pretrained model, you can embark on lexicon-free and lexicon-based recognition tasks. Simply refer to the functions recognizeImageLexiconFree and recognizeImageWithLexicon in the utilities.lua file for details.

Training Your Own Model

If you wish to train a new model on your dataset, follow these steps:

1. Create a new LMDB dataset using the provided Python program in tool/create_dataset.py.
2. Create a model directory under model, e.g., model/foo_model, and create a configuration file config.lua in this directory.
3. Go to the source directory and execute:
   th main_train.lua ..models/foo_model.

Building with Docker

If you prefer Docker for your environment, here’s how:

1. Install Docker by following the instructions here.
2. Install nvidia-docker by following the instructions here.
3. Clone the repository and run the following:
   docker build -t crnn_docker .
4. Run Docker using:
   nvidia-docker run -it crnn_docker.

Troubleshooting

If you encounter any issues during installation or execution, consider checking the following:

• Ensure all dependencies are correctly installed.
• Verify that you are using a compatible version of Ubuntu.
• Check GPU drivers and ensure they are properly set up.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.