OCR Accuracy Guide: How to Ensure Precision and Improve Results

OCR (Optical Character Recognition) accuracy is the foundation of every serious document automation project: if the extracted text isn't reliable, downstream processes never will be. For those leading operations and IT in finance, logistics, insurance, and manufacturing contexts, this means one simple thing: understanding how to measure accuracy, how to continuously improve it, and what to demand from a modern document processing solution.

What "OCR accuracy" means

OCR accuracy refers to the software's ability to convert text images (scanned PDFs, photos, email attachments) into editable digital text with the fewest possible errors. High accuracy translates to fewer manual corrections, fewer operational errors, and greater trust in automated flows feeding ERP, CRM, and management systems.

The main factors influencing accuracy are:

• Document quality: blurring, shadows, stains, crumpled documents, or low resolution drastically lower results.
• Characters and formatting: decorative fonts, very dense tables, multiple columns, and complex layouts make recognition harder.
• Language and symbols: multilingual documents, sector symbols, technical abbreviations, and specific lexicon require OCR engines trained on the domain.

Three myths to eliminate immediately

To design well, the first step is to free yourself from some false beliefs:

"OCR works well with any document"

Performance always depends on image quality, layout, and content type. A "perfect" document for the human eye isn't necessarily perfect for an OCR engine.

"All OCR tools handle handwriting without problems"

Handwriting recognition is an advanced feature: it requires dedicated models, extensive training, and isn't available (nor at the same level) in all solutions.

"OCR accuracy is fixed"

Modern AI and Machine Learning-based systems improve over time: the more you feed them with real data and feedback, the more they learn to handle exceptions, rare layouts, and niche cases.

Having realistic expectations helps set measurable objectives and evaluate solutions comparably, instead of stopping at generic "99% precision" promises.

How to measure OCR accuracy

To talk about OCR accuracy, you need objective metrics based on comparing output with "ground truth" (a correct version of the text). The main ones are:

Error-based metrics

• Character Error Rate (CER): percentage of wrong characters out of total. Calculated from substitutions, insertions, and deletions needed to transform OCR text into correct text.
• Word Error Rate (WER): same logic, but at word level. Useful when text readability or semantic content matters most.
• Line Error Rate (LER): percentage of incorrectly recognized lines out of total.

Precision-based metrics

Widely used, especially in business, also the "positive" versions:

• Character Accuracy Rate (CAR): percentage of correct characters. Example: 950 correct characters out of 1,000 → CAR 95%.
• Word Accuracy Rate (WAR): percentage of correct words, particularly relevant for documents where each word represents critical data (invoices, delivery notes, bills, packing lists).

Indicatively, for printed text and well-acquired documents, a CER in the order of 1–2% is considered good; between 2–10% quality is average; beyond 10% it becomes difficult to use output without strong human intervention.

Why OCR accuracy matters more in certain sectors

Almost all business departments use "transactional" documents, i.e., documents that enable or certify a transaction. In these contexts, OCR accuracy isn't "a detail," but a process requirement.

Some examples:

Finance and administration

Extracting amounts, IBANs, customer/supplier codes, due dates from invoices and credit notes. Errors here mean wrong reconciliations, incorrect payments, longer cycle times.

Logistics and customs

Shipping labels, bills, packing lists, customs documents. A wrong field can block a shipment or generate unexpected extra costs.

Sales and customer service

Sales orders received via email, PDF, portals. Errors in extracting item codes, quantities, or addresses turn into duplicate orders, wrong shipments, complaints.

Healthcare and insurance

Prescriptions, medical records, reimbursement requests. Here error tolerance is minimal, due to clinical, legal, and reputational impacts.

In summary: the "acceptable" accuracy level depends on the risk connected to a single error on that document.

OCR and handwriting: what to realistically expect

Handwriting is historically one of the most complex cases for traditional OCR. However, more recent technologies based on deep neural networks have reduced the gap compared to printed text.

In practice:

• Models analyze shape, context, and character sequence to "predict" the most likely writing.
• They work much better with printed writing (readable blocks, separate letters) than with fast, slanted, or very personal cursive.
• On document sets with very different handwriting, it often remains necessary to insert a human validation step on critical fields to ensure data quality.

If handwriting is central to your flows (e.g., counter forms, historical paper documents, questionnaires), it's essential to test any OCR solution directly on your real samples, with CER/WER measurements before an extended rollout.

How to improve OCR accuracy (concretely)

OCR accuracy isn't a constant: you can work on it, and a lot. The three pillars are: input quality, intelligent pre-processing, right model.

1. Document quality care

• Scans at least 300 DPI to reduce blurring.
• Flat, well-lit documents, without heavy shadows, stains, or reflections.
• Avoid "on-the-fly" photos with strong tilt or margin cuts.

2. Automatic pre-processing

• Noise reduction, binarization, contrast normalization.
• Skew correction (crooked documents) and border alignment.

In the modern world, these phases are typically orchestrated by the Intelligent Document Processing platform and don't require manual intervention.

3. Training and domain knowledge

• Train the model on the company's real documents (language, layout, typical error cases).
• Integrate sector dictionaries and constraints (IBAN formats, item codes, customs codes, internal nomenclatures) to help the algorithm choose the most plausible interpretation.
• Implement a "feedback loop" cycle: what the user corrects once becomes a training signal to improve extraction next time.

4. Technology: legacy templates vs modern AI

• Systems based only on rigid templates work as long as layouts and suppliers don't change; when they do, continuous maintenance is needed.
• AI and Machine Learning-based solutions adapt better to different fonts, formats, and languages, reducing dependencies on rigid rules and allowing scaling to new document types without starting from scratch every time.

Better to build in-house or adopt a platform?

Developing an internal OCR solution gives the feeling of maximum control but brings hidden costs:

• You need a team with advanced skills in Machine Learning, MLOps, data engineering, UI/UX, and integration with existing systems.
• You must manage collection, annotation, and maintenance of updated training datasets, plus deploy, monitoring, and continuous model fine-tuning.
• The risk is concentrating energy on "making technology" instead of improving business processes.

The pragmatic choice: specialized platform

For many companies, the most pragmatic choice is to adopt a specialized platform that offers:

• OCR/IDP engines already trained on typical use cases (orders, invoices, logistics documents, healthcare, insurance).
• A "no-code/low-code" configuration model to quickly adapt extraction to specific fields and rules.
• Ready integrations with ERP, CRM, and vertical systems, reducing time-to-value.

Quick checklist for evaluating an OCR tool

To close, an operational summary that helps you evaluate any solution (or internal project):

Processes and use cases

• Which document processes do you want to automate as priority (order-to-cash, procure-to-pay, claims management, etc.)?
• What document types and volumes do you need to handle today, and what do you expect in 12–24 months?

Metrics and transparency

• Does the solution expose CER, WER, CAR, WAR on your real documents (not just theoretical benchmarks)?
• Can you run a POC with representative datasets before committing long-term?

Integration and scalability

• Are there ready APIs, connectors, or integrations to your ERP/CRM/management systems?
• How does the platform behave as volume grows (peaks, seasonality, new business lines)?

Customization and learning

• Can you define custom fields, business rules, validations, and checks without rewriting templates from scratch?
• Does AI learn from user feedback and progressively reduce validation time?

Security and compliance

• How is data protection managed (encryption, retention, data residency, access logs)?
• Is the solution compliant with relevant regulations (e.g., GDPR) for your sector and geographic area?

Cost and ROI

• Is the pricing model consistent with your scenario (per page, per document, per automated process)?
• Can you quantify the benefit in terms of hours saved, errors avoided, and cycle time reduction?