Optical character recognition is a technology that allows machines to recognize and interpret characters or symbols from images or scanned documents. Neural network is a type of machine learning algorithm that helps with the recognition and interpretation process.
OCR using neural network works by first scanning an image or document and converting it into a digital format. The neural network then analyzes the image and identifies patterns in the characters. These patterns are used to determine the correct characters and convert them into machine-readable text.
OCR using neural network offers several benefits, including increased accuracy in character recognition, the ability to handle different fonts and languages, and the ability to learn and improve over time. It also allows for faster and more efficient processing of large amounts of text.
The accuracy of OCR using neural network can vary depending on the quality of the scanned image, the complexity of the characters, and the training of the neural network. However, with proper training and optimization, it can achieve high levels of accuracy, often close to or even surpassing human accuracy.
Yes, OCR using neural network can also be used for handwriting recognition. However, it may require more advanced algorithms and training to accurately recognize handwritten characters compared to printed characters. Additionally, the accuracy may also depend on the legibility of the handwriting.