OCR Engine: Tesseract OCR
Last updated
Last updated
For text extraction, the Document Understanding Subnet currently utilizes Tesseract OCR, an open-source and highly efficient OCR engine. Tesseract OCR plays a critical role in converting visual text data into structured, machine-readable formats, enabling the subnet to analyze and categorize text with ease. We are also developing a proprietary OCR engine to further enhance text extraction accuracy and processing speed.
An OCR engine typically consists of several components that work together to process images and extract text. The core functionality of OCR involves image preprocessing, character recognition, and post-processing.
Image Preprocessing: This initial step improves the quality of the input image to enhance recognition accuracy. Techniques such as noise reduction, binarization, normalization, and deskewing are applied to ensure that the text is clear and readable. For example, binarization converts a grayscale image to black and white, making the text stand out against the background.
Character Recognition: This is the heart of the OCR process. Here, algorithms identify characters within the preprocessed image. Traditional methods include template matching and feature extraction, while modern OCR systems increasingly rely on machine learning techniques, especially deep learning. Convolutional Neural Networks (CNNs) have become particularly effective at recognizing characters in various fonts and styles.
Post-Processing: After recognition, post-processing is employed to enhance accuracy further. This step often includes spell checking, context analysis, and formatting adjustments. By utilizing dictionaries and language models, the OCR engine can correct misrecognized words and improve overall output quality.
