Hello everyone, my WeChat AI, today I will introduce you to the deep learning practice 43-OCR function collection [OCR basic principle + OCR text segment merging + OCR recognition of PDF scanned files], OCR technology is based on image preprocessing, feature extraction, Steps such as character classification and post-processing enable automated character recognition by converting printed characters into a computer-processable text form. With the development of technology, OCR system has been widely used in many fields, such as text recognition, document digitization and automatic data processing.
Basic principles of OCR
The OCR function in this article is based on the OCR function developed by PaddleOCR. PaddleOCR is an end-to-end OCR toolkit developed based on the PaddlePaddle deep learning platform for text detection and text recognition tasks. It is based on convolutional neural network and combines the steps of preprocessing, feature extraction, text detection and text recognition to achieve accurate and reliable OCR function. The following is an introduction to the principle of PaddleOCR:
1. Data preparation and preprocessing: First, PaddleOCR needs to prepare the data set for training and perform necessary preprocessing. This includes converting the image to a format suitable for the input model, performing data augmentation operations such as rotation, scaling, cropping, etc., and annotating text box information.
2. Text detection: PaddleOCR uses a text detection model based on deep learning, such as EAST (Efficient and Accurate Scene Text Detector) or PSENet (Shape Robust Text Detection with Progressive Scale Expansion Network), to detect text areas in images. This model learns the image by