First-generation optical character recognition systems relied on rigid matrix matching: comparing pixel grids directly against stored font templates. If a scan was slightly rotated, degraded by fax static, or printed in an unusual font, recognition accuracy plummeted.

Modern OCR conversion uses convolutional neural networks that extract high-level stroke features—crossbars, ascenders, descenders, and curve trajectories. Contextual language models evaluate adjacent characters to eliminate ambiguities (such as distinguishing between "cl" and "d").

By compiling these models into WebAssembly, PDF Converter Lab delivers modern recognition capabilities directly inside standard web browsers without sacrificing privacy.