Mechanics and Challenges of OCR

The backbone of any OCR-IDP system is a well-defined workflow that starts with image capture and concludes with text extraction. Images are initially preprocessed to enhance quality, a step that often involves noise reduction and normalization. Following preprocessing, machine learning algorithms like Convolutional Neural Networks (CNNs) are applied for feature extraction, while Recurrent Neural Networks (RNNs) handle sequence modeling to transform the image into text.

However, the OCR-IDP journey isn't without its challenges. While the technology has made leaps in accuracy, it still grapples with issues like recognizing cursive handwriting or interpreting text from poor-quality images and inconsistent lighting conditions.

To tackle these challenges, Statigen has leveraged a range of OCR tools and techniques tailored to specific project requirements. For instance, we've used Tesseract with Python to successfully extract content from healthcare data in pdfs and images. In more complex scenarios requiring real-time image reading, our teams have employed YOLO v3. To improve model accuracy in these real-time scenarios, we've enhanced YOLO v3 using synthetic data generated through Generative Adversarial Networks (GANs). This innovative approach has proven particularly effective in situations where the availability of real-world data is limited.

IDP goes beyond simple text extraction to understand the context, classify the document, and even make sense of unstructured data. It often leverages OCR as one of its components but combines it with other technologies like Natural Language Processing (NLP), Machine Learning (ML), and rule-based algorithms to offer a more comprehensive solution.