Data entry team keying numbers from PDFs and scanned documents into your system all day?
OCR attempts that failed because document layouts vary or scan quality is inconsistent?
OCR Development Services
Manual data entry from documents is slow, error-prone, and scales linearly with volume. When the invoice pile doubles, so does the headcount. When the scan quality drops, so does the accuracy. When the document format changes, the process breaks.
We build production OCR systems that read your specific documents accurately -- with AI-powered extraction, validation pipelines, and exception handling for the cases where the system needs a human. We've shipped industrial OCR systems deployed in real production environments.
Production OCR systems built for your document types -- not generic demos
AI-powered extraction with confidence scoring and human review for exceptions
Structured data output delivered to your ERP, database, or downstream system
Built and shipped a production gas station fuel delivery invoice OCR system
Trusted by startups & global brands worldwide
OCR is not solved by an API call
Every "OCR" demo looks impressive on clean, formatted documents. Production systems deal with scans at an angle, handwriting on pre-printed forms, faxed documents, photos taken on a phone in poor lighting, and vendor invoice formats that change without notice.
The hard part is not reading the text. It's extracting the right fields from variable layouts, validating them against business rules, routing the exceptions to the right people, and delivering clean data to a system that needs it in a specific format.
We shipped a gas station fuel delivery invoice OCR system -- thousands of invoices a month, multiple supplier formats, processing from email attachment to ERP posting without human data entry. That's the production-grade OCR we build.
What the system includes
Document ingestion
Automated capture from email attachments, upload portals, network folders, and API submission. Multi-format support -- PDF (digital and scanned), JPEG, PNG, TIFF, and multi-page documents. Deduplication to prevent double processing. Document queuing and processing status tracking. The intake layer that works however your documents arrive.
Pre-processing and enhancement
Image pre-processing for challenging scans -- deskewing, contrast enhancement, noise removal, resolution normalisation. Multi-page document splitting and page classification. Orientation detection and correction. The image quality improvements that make OCR on real-world documents viable.
Field extraction
Extraction of the specific fields your business needs -- headers, line items, totals, dates, reference numbers, and custom fields. Template-based extraction for known document formats. AI-powered extraction for variable or unknown formats. Table extraction for line-item data. Confidence scoring for every extracted field.
Validation and business rules
Field-level validation -- format checks, range checks, required field presence, and cross-field consistency. Business rule validation -- totals that add up, dates that make sense, vendor codes that exist in your system. Lookup validation against your reference data. Failed validation routes to exception review rather than failing silently.
Exception review interface
Web interface where operators review flagged extractions. Original document and extracted fields displayed side by side. Field-level correction with one-click accept. Batch review mode for high-volume exception queues. Corrections fed back into the extraction system. Processing metrics and exception rate tracking.
Output and integration
Structured output delivered to your downstream system -- JSON via API, SQL writes to your database, XML to your ERP, CSV for manual consumption. Output schema designed to match your target data model exactly. Delivery triggered by processing completion or on a schedule. Full processing audit trail for every document.
Tell us about the documents you need to extract data from.
Type, volume, current accuracy problems. We'll design the system and give you a fixed cost.
Frequently asked questions
Custom OCR development is the process of building an optical character recognition system designed for your specific document types, extraction requirements, and output destinations -- rather than a generic OCR API that reads text but doesn't extract structure. A custom OCR system reads your documents, understands which fields matter, extracts them accurately, validates the output against your business rules, and delivers clean structured data to your downstream system. We've built production OCR systems for industrial environments where accuracy and throughput matter.
For clean, digital PDFs, accuracy is typically 97--99%. For scanned documents, accuracy depends on scan quality -- resolution, skew, noise, and contrast. We improve accuracy for challenging scans through pre-processing (image enhancement, deskewing, contrast normalisation), vendor-specific extraction templates for high-volume document sources, AI-based fallback for fields that rule-based extraction misses, and confidence scoring that routes low-confidence extractions to human review. Most production systems we build reach 85--95% straight-through processing.
Layout variation is the hardest problem in OCR. The same invoice from the same vendor might be formatted differently depending on the system it was generated from. We handle variation through a combination of adaptive template matching (the system selects the best extraction template for each document based on layout features), AI-powered extraction that generalises better than rule-based approaches, and exception queues where high-variation documents go to human review with guided extraction. For known high-volume vendors, we build specific extraction rules that give the best accuracy.
Every production OCR system we build has an exception path. Low-confidence extractions and documents that fail validation go to a human review queue. Reviewers see the original document and the extracted fields side by side, correct any errors, and confirm the output. Corrections feed back into the system to improve future accuracy for similar documents. The exception path is designed to be fast -- a reviewer handles an exception in under 60 seconds. The goal is high automation rates with a clean fallback for the cases that need a human.
We've built production OCR systems for: invoices (our gas station fuel delivery case -- thousands of invoices per month, automated from receipt to ERP posting), purchase orders, delivery notes and packing lists, forms and applications, identity documents for KYC, shipping labels and customs documents, industrial inspection reports, and certificates of analysis. The extraction requirements differ significantly by document type. We design the extraction approach based on your specific document characteristics.
A focused OCR system -- one document type, extraction of 5--15 fields, validation, and output to one target system -- typically runs $20,000--$50,000. Multi-document type platforms with exception workflows, human review interfaces, and multiple output integrations run $50,000--$120,000. We've built industrial-grade production systems across this range. We scope every project before pricing it.