Yes, Optical Character Recognition (OCR) often employs machine learning techniques. OCR is the process of converting different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data.
Here's how machine learning is integrated into OCR:
- Pattern Recognition: Traditional OCR systems were based more on pattern recognition, where the software was programmed with a set of predefined patterns (like different font styles and sizes) to recognize characters. This approach had limitations in dealing with variations in text appearance.
- Machine Learning Algorithms: Modern OCR systems use machine learning algorithms, especially deep learning methods like Convolutional Neural Networks (CNNs). These algorithms allow the system to learn from a large dataset of texts in various fonts, styles, and quality. The system essentially learns to recognize characters and words in much the same way a human would, by looking at numerous examples.
- Adaptability and Accuracy: Machine learning-based OCR systems are more adaptable and accurate, especially with complex layouts, diverse fonts, and poor image quality. They can handle unstructured data better and are more resilient to noise and distortions in the input images.
- Continuous Improvement: As more data is processed, these systems can continue to learn and improve, making them more efficient over time. This is a significant advantage over traditional pattern recognition methods.
OCR has evolved significantly with the integration of machine learning, making it more powerful, versatile, and accurate. HelloData.ai has used OCR for document extraction, including from multifamily floorplans with our floorplans.ai product.