Optical Character Recognition (OCR) is a technology that converts images of text into machine-readable text data, and its development spans over a century of innovation from early mechanical devices to modern neural network-based systems.

The Tesseract OCR engine, originally developed by Hewlett-Packard in the 1980s and later open-sourced by Google, is one of the most widely used OCR engines. Tesseract's recognition pipeline begins with page layout analysis, which identifies text regions, columns, paragraphs, and lines within the image. It then segments each text line into individual characters or words. In its modern LSTM (Long Short-Term Memory) neural network mode, Tesseract processes entire text lines rather than individual characters, using a recurrent neural network that reads sequences of pixel columns from left to right (or right to left for RTL scripts) and outputs the most probable character sequence.

Character segmentation is one of the most challenging aspects of OCR because characters in printed and handwritten text often touch, overlap, or have ambiguous boundaries. Traditional OCR systems use connected component analysis to identify individual character shapes, then classify each shape by comparing it against trained character templates. However, this approach struggles with ligatures (connected characters like "fi" in many fonts), degraded or noisy images, and scripts like Arabic where characters naturally connect. Modern neural network approaches sidestep the segmentation problem entirely by processing entire word or line images and using sequence-to-sequence models that learn to output character sequences directly from pixel data without explicit segmentation.

Language models play a crucial role in improving OCR accuracy beyond what pure character recognition can achieve. After the neural network produces its initial character predictions with associated confidence scores, a language model evaluates the resulting text for linguistic plausibility. If the character recognizer is uncertain between "h" and "b" in a particular position, the language model can determine that "the" is far more probable than "tbe" in English context and correct the output accordingly. This is similar to how smartphone keyboard autocorrect works. Modern OCR systems use statistical language models trained on large text corpora, and some use word-level dictionaries as an additional constraint. The combination of visual recognition and linguistic context is what enables modern OCR to achieve 99%+ accuracy on clean printed text, a remarkable feat considering the enormous variation in fonts, sizes, and printing quality across documents.