Tesseract OCR

What is OCR?

Optical Character Recognition (OCR) is the process of converting images containing typed or handwritten text—such as scanned documents, photos, or screenshots—into machine-readable and searchable text.

Tesseract OCR

Tesseract OCR is one of the PDF Document Vision options in Assistivox AI. It processes images through a multi-stage pipeline that combines traditional computer vision with modern AI approaches:

AI Architecture: Tesseract uses deep learning techniques to recognize characters and understand document layout. It is a trained AI model that has learned to recognize text patterns from extensive datasets.

Multi-Stage Processing: The engine first analyzes the visual structure of the image, identifies text regions, recognizes individual characters, and then applies language models to improve accuracy by understanding context and correcting obvious errors.

Layout Analysis: Beyond character recognition, Tesseract attempts to understand document structure, identifying text blocks, paragraphs, and reading order to maintain the logical flow of the original document.

Training Data and Models

Tesseract is trained on extensive datasets to provide robust text recognition in different scenarios:

Training Scale: For Latin-based languages, standard Tesseract models are trained on approximately 400,000 text lines spanning about 4,500 different fonts. This includes text styles found in books, magazines, digital documents, and real-world printed materials.

Dataset Composition: The training corpus includes high-quality, clean synthetic images and more challenging real-world samples with typical variations found in scanned documents.

Performance and Quality

Tesseract's performance varies with input quality and document characteristics:

Optimal Conditions: With high-quality, clear scans of printed text using standard fonts, accuracy commonly exceeds 90%. Performance is particularly strong on clean documents with good contrast and standard layouts.

Real-World Performance: Accuracy decreases with poor-quality images, noisy scans, skewed documents, or unusual fonts. Complex layouts, handwriting, and degraded image quality can substantially impact recognition rates.

Best Use Cases: Tesseract excels with clean scans of printed documents using standard fonts. It performs well on books, magazines, typed letters, and digital document printouts where text is clear and well-formatted.

Tesseract vs. Docling

While both are document processing options in Assistivox AI, they serve different purposes:

Tesseract is specifically an OCR engine designed for scanned documents, photos, or PDFs that contain text as images. It uses AI-powered character recognition to convert visual text into machine-readable format.

Docling is a text extraction system designed for PDFs with embedded, selectable text. Rather than using OCR, docling reads existing text data directly from the PDF structure and reconstructs the document's logical organization.