Our Approaches
Our solutions for complex OCR/OCV leverage state-of-the-art machine learning algorithms and synthetic image data to train robust models capable of handling diverse scenarios. By simulating different text appearances and environmental conditions through 3D rendering and generative AI, we ensure our models are well-prepared to recognize and verify text accurately under various conditions.
We begin by analysing the specific OCR/OCV needs of our clients, including the types of text and documents they process. Using this information, we create synthetic datasets that replicate these conditions, incorporating variations in font styles, sizes, orientations, and backgrounds. Our generative AI techniques add realistic noise and distortion patterns, ensuring that the models can handle real-world challenges.
Our approach also includes continuous model optimization and validation using real-world data, ensuring that our systems remain accurate and reliable over time. We provide tools for easy integration with existing workflows, enabling seamless adoption and deployment of our OCR/OCV solutions.
