AI image processing detects and recognizes text in images through a combination of computer vision techniques and deep learning models, primarily using Optical Character Recognition (OCR) systems. Here's how it works:
Image Preprocessing: The input image is first enhanced to improve text visibility. This may include resizing, noise reduction, contrast adjustment, and binarization (converting the image to black and white) to make text stand out from the background.
Text Detection: The system identifies regions in the image where text is likely to be present. This is done using algorithms like Convolutional Neural Networks (CNNs) or specialized models such as EAST (Efficient and Accurate Scene Text Detector) or CRAFT (Character Region Awareness for Text Detection). These models locate bounding boxes around text areas, even if the text is curved, skewed, or multi-oriented.
Text Recognition: Once text regions are detected, the next step is to recognize the actual characters. This is handled by Recurrent Neural Networks (RNNs) or Transformer-based models like CRNN (Convolutional Recurrent Neural Network) or Tesseract OCR (an open-source engine). Advanced models like TrOCR (Transformers for OCR) use Vision Transformers to directly process the image patches and predict text sequences.
Post-Processing: The recognized text may undergo correction to fix errors, using language models or dictionaries to ensure the output is coherent and accurate.
Example: Suppose you have an image of a street sign with the text "No Parking". The AI system first preprocesses the image to enhance the sign's visibility. Then, the text detection model locates the rectangular area containing the text. Next, the recognition model decodes the letters and outputs "No Parking". If the sign is partially obscured or at an angle, the model adjusts its detection and recognition accordingly.
In cloud-based applications, services like Tencent Cloud OCR can be used to integrate such text detection and recognition capabilities into applications, providing scalable and efficient solutions for extracting text from images in various formats, such as scanned documents, product labels, or license plates.