Optical Character Recognition (OCR)
Technology that converts images of text — scanned documents, photos, PDFs — into machine-readable text that can be processed by software.
OCR is the entry point for processing physical or scanned documents. Before any NLP, embedding, or LLM analysis can occur, the text must be extracted from the document image. Modern OCR engines use convolutional neural networks and transformer architectures to achieve high accuracy even on degraded scans, handwriting, and complex layouts with tables, headers, and multi-column text.
OCR quality directly affects the accuracy of all downstream document intelligence tasks. OCR errors — misread characters, merged words, incorrectly ordered text boxes — propagate through the pipeline and reduce extraction accuracy. For legal and compliance documents where precision matters, OCR quality is not just a technical detail; it is a risk management consideration. Document intelligence platforms that include OCR quality scoring and human-in-the-loop review for low-confidence sections provide higher reliability for regulated use cases.
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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