Hire Tesseract OCR Engineering
for intelligent document processing

From invoice extraction to large-scale document digitisation, our OCR engineers build accurate, on-premise text recognition pipelines powered by Tesseract and computer vision.

Discuss your OCR project

25+

OCR pipeline projects delivered

100+

languages supported

30+

AI & computer vision engineers

Core Capabilities

What we build
with Tesseract OCR

Document Digitisation Pipelines

Scanned PDFs, invoices & forms

End-to-end document digitisation — image pre-processing with OpenCV, Tesseract recognition, structured data extraction, and output to JSON, databases, or downstream business systems.

Intelligent Data Capture

Structured extraction from unstructured docs

Beyond raw text recognition — we apply NLP and regex post-processing to extract named entities, dates, amounts, and table data from invoices, contracts, and identity documents at scale.

On-Premise OCR Systems

Air-gapped, data-sovereign deployments

Fully containerised Tesseract pipelines that run within your own infrastructure — no cloud dependency, no data leaving your network. Ideal for regulated industries handling sensitive documents.

How It Works

From raw image to
structured data

Image Pre-
Processing

Raw scans are enhanced with OpenCV — deskewing, denoising, binarisation, contrast normalisation, and resolution upscaling — to give Tesseract the best possible input for accurate recognition.

OCR
Recognition

Tesseract LSTM engine processes pre-processed images with configured page segmentation modes, language models, and custom-trained data where needed — producing raw text and bounding box coordinates.

Post-Processing &
Extraction

NLP pipelines clean OCR output, extract structured fields, validate against business rules, and flag low-confidence regions for human review — minimising manual correction effort.

Integration &
Monitoring

Structured data is pushed to databases, ERPs, or APIs. Our DevOps engineers deploy the pipeline on Kubernetes with throughput dashboards and accuracy monitoring.

Hire OCR Engineers

OCR specialists ready
to join your team

Scale your document intelligence capabilities with dedicated OCR engineers who deliver accurate, production-ready pipelines from day one.

Hire OCR Engineers

Image pre-processing with OpenCV for maximum OCR accuracy

Custom Tesseract LSTM model training for domain-specific documents

NLP post-processing for structured entity and table extraction

On-premise Docker & Kubernetes deployment for data sovereignty

Human-in-the-loop review workflows for low-confidence regions

AI + Tesseract OCR

Documents that don't just
scan — they understand

LLM document
understanding

Tesseract extracts raw text; LLMs turn it into meaning — summarising contracts, answering questions about invoices, and classifying document types without any manual rule writing.

AI-powered
validation

Machine learning classifiers validate extracted fields against business rules — catching OCR errors, flagging anomalies, and routing exceptions to human reviewers automatically.

Continuous
model improvement

Human corrections feed back into training pipelines — Tesseract fine-tuning and NLP model retraining improve accuracy over time without manual intervention from your team.

RAG over
document archives

OCR-extracted text is chunked, embedded, and indexed into vector stores — enabling RAG pipelines that let users query entire document archives with natural language questions.

FAQ

Frequently Asked
Questions

Tesseract handles a wide range of documents — scanned PDFs, invoices, receipts, identity documents, handwritten forms, medical records, and more. We pre-process images with OpenCV (deskewing, denoising, contrast enhancement) to maximise recognition accuracy before passing them to Tesseract.

With proper image pre-processing and custom model training, Tesseract achieves 95–99% character accuracy on clean documents. For complex layouts or degraded scans, we combine Tesseract with deep learning models (CRNN, DocTR) or hybrid cloud approaches to hit production-grade accuracy targets.

Yes — Tesseract is open-source and fully on-premise deployable. We containerise OCR pipelines with Docker, orchestrate with Kubernetes, and integrate with your existing document management systems — keeping sensitive data within your infrastructure without any cloud dependency.

Tesseract supports 100+ languages with trained LSTM models. For multi-column layouts, we combine Tesseract's page segmentation modes with custom layout analysis using OpenCV contour detection — ensuring text is extracted in the correct reading order regardless of document complexity.

Absolutely. We build intelligent document processing pipelines where Tesseract handles raw text extraction, which is then fed into LLMs or NLP models for entity recognition, classification, summarisation, and structured data extraction — turning unstructured documents into actionable data.