Overview / Usage

Overview / Usage

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The Intel Handwriting Recognition Suite is an advanced artificial intelligence project designed to tackle the challenge of accurately recognizing and digitizing handwritten text. This project focuses on developing a robust and efficient handwriting recognition system using deep learning models, specifically tailored for deployment on Intel hardware, optimized with Intel oneAPI, and utilizing Intel-optimized TensorFlow.

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The main problem being solved is the automation of converting handwritten text into digital format, which is applicable in various industries such as banking (check processing), healthcare (digitizing handwritten records), and education (automating the grading of handwritten exams). This solution enhances efficiency and reduces human error in the digitization process.

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The project is intended to be used in production environments where high accuracy and performance are critical, such as real-time text recognition in mobile devices, digital scanning solutions, and automated data entry systems. The integration with Intel's technologies ensures that the system can handle large-scale data processing efficiently while maintaining accuracy.

Methodology / Approach

Methodology

The project employs a comprehensive methodology combining advanced deep learning techniques with Intel's hardware and software optimization tools. Here's an outline of the approach:

1. Data Preprocessing and Augmentation:

• The MNIST dataset is used for initial training, with additional preprocessing steps to normalize, reshape, and augment the data. This ensures the model is robust against various handwriting styles and environmental factors.

1. Model Development:

• A Convolutional Recurrent Neural Network (CRNN) architecture is used, combining the strengths of CNNs (for feature extraction) and RNNs (for sequence learning). This hybrid approach allows the model to learn spatial hierarchies in images and temporal dynamics in sequences.

1. Hyperparameter Optimization:

• Keras Tuner is employed to fine-tune the model's hyperparameters, ensuring optimal performance. This step involves exploring various learning rates, batch sizes, and model configurations to identify the best combination for the task.

1. Model Optimization and Deployment:

• The trained model is optimized using Intel's TensorFlow optimizations, including quantization techniques to reduce model size and increase inference speed. The final model is deployed on Intel hardware such as the Intel Movidius Neural Compute Stick, allowing for efficient real-time handwriting recognition.

1. Web Interface for Interaction:

• A Flask-based web interface is developed to provide an easy-to-use platform for users to interact with the model, upload handwritten samples, and receive digital text outputs.

Technologies Used

Technologies Used

Software and Libraries:

• TensorFlow: Used for building and training the deep learning model.

• Keras Tuner: For hyperparameter tuning and optimization.

• Flask: A lightweight web framework for building the web interface.

• NumPy, Matplotlib: Supporting libraries for data manipulation and visualization.

Intel Technologies:

• Intel® oneAPI: For optimizing the model performance across Intel architectures.

• Intel-optimized TensorFlow: Ensures the deep learning model runs efficiently on Intel hardware by leveraging optimizations like Intel's MKL (Math Kernel Library).

• Intel Movidius Neural Compute Stick: A hardware accelerator for deploying the optimized model for real-time inference on edge devices.

Hardware:

• Intel CPUs: Leveraged during the development phase for model training and optimization.

• Intel Movidius Neural Compute Stick: Used for deploying the model in production, enabling fast, power-efficient inference on edge devices.

This combination of advanced AI techniques, powerful optimization tools, and high-performance Intel hardware ensures that the Intel Handwriting Recognition Suite is both accurate and efficient, suitable for real-world applications in various industries.

Repository

https://github.com/ahmadabuhattab/identify-handwriting-ai-intel