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OCR is a technology that converts different types of documents, such as scanned paper documents, PDFs or images captured by a digital camera, into editable and searchable data.

Deskewing corrects the alignment of an image by detecting and fixing any slant or irregular orientation, which is essential for accurate character recognition.

Feature extraction involves identifying and extracting key features from character segments that help the OCR algorithm differentiate between various characters.

The language model in OCR is used to predict the likelihood of a sequence of characters or words, helping to resolve ambiguities and improve the accuracy of the text recognition.

Character Segmentation is the process of separating text into individual characters, which is critical for character recognition, especially in cursive or non-standard fonts.

Binarization is the process of converting a grayscale image into a binary image, where each pixel is either black or white, to simplify the analysis for OCR.

Preprocessing involves preparing the raw image for OCR by enhancing it through noise reduction, binarization, and normalization, to improve the accuracy of text recognition.

Pattern recognition is the core of OCR, where the system uses algorithms to identify and classify the shapes of characters within the segmented image.

Neural networks, particularly Convolutional Neural Networks (CNNs), are used in modern OCR systems to classify characters based on learned features from large datasets of text.

Adaptive thresholding is a technique used during binarization that adjusts the threshold for different regions of the image based on local image characteristics, facilitating better foreground and background separation.

Text line detection involves identifying and separating lines of text within the image, which is necessary for processing multi-line documents and maintaining the structure of the text.