The process of AI text recognition usually includes the following steps:
Image preprocessing: The input image is cleaned, denoised, enhanced and other operations to improve the accuracy of subsequent recognition.
Text positioning and segmentation: Use image processing techniques to locate text areas in an image and separate the text from the background. This step can be particularly challenging for complex layouts or documents in multiple languages.
Feature extraction: Extracting features from segmented text images, which can be based on shape, texture, structure, or other properties.
Model training: The use of large amounts of labeled data (i.e. data sets of known words and their corresponding images) to train machine learning models, usually deep learning models, such as convolutional neural networks (CNNS) or recurrent neural networks (RNNS).
Text recognition: A trained model is applied to a new image to recognize and extract text from it.
Post-processing: Correction, formatting, translation (if necessary), and other subsequent processing of the recognized text.
Artificial intelligence character recognition technology has been widely used in many fields, such as document digitization, automatic data entry, license plate recognition, and reading assistance for the visually impaired. With the continuous progress of technology, the accuracy and efficiency of artificial intelligence character recognition are constantly improving, bringing great convenience to various application scenarios.
Industry Encyclopedia>Artificial intelligence word recognition
Artificial intelligence word recognition
2024-03-21 10:19:20