OCR-Powered Invoice Automation with Tesseract & EasyOCR

July 31, 2025
Satheeskumar
Merfantz Developments
0

1. Introduction: The Need for Automation

Manual invoice processing was once a tedious and error-prone routine for the accounts team, leading to time delays, inefficiencies, and data inconsistencies. With the increasing volume of over 900 invoices per month, the demand for a scalable, automated solution became urgent.

This case study explores how we leveraged OCR (Optical Character Recognition) combined with custom data extraction algorithms to digitize invoice processing and automatically sync extracted data into Sage Accounting software, significantly improving operational efficiency and accuracy.

This transition marked a critical shift toward Intelligent Document Processing, enabling a foundation for further innovation in finance using Machine Learning and Automation techniques.

2. Business Challenges

Before automation, the finance team faced significant hurdles in managing over 900 invoices each month, all of which were processed manually. This approach was not only time-consuming but also highly prone to human errors, particularly in key fields like invoice amounts, vendor names, and due dates.

The manual entry process led to delays in updating records in Sage Accounting, which in turn affected timely reporting and payment cycles. Auditing and tracking invoices became increasingly complex as the volume grew, resulting in scattered data and lack of version control.

Moreover, without centralized digitization, there was limited visibility and control over the end-to-end finance workflow, making it difficult to monitor performance or identify bottlenecks effectively.

3. Can We Trust the Answers We Get from This Engine Result?

Yes but with intelligent checks in place.

Our OCR engine was built with accuracy, consistency, and validation as its core pillars. While OCR alone can sometimes misinterpret characters or misalign data, we addressed this by developing a multi-layered validation process that ensures trust in the extracted results:

Field-Level Confidence Scoring: Each extracted entity (like amount, invoice number, vendor) is tagged with a confidence score. If it falls below a set threshold, it’s flagged for manual review.
Cross-Validation Rules: Extracted data is validated using business logic—e.g., invoice totals are checked against line item sums, due dates must follow invoice dates, and vendor names must match known records.
Fallback Mechanism: When OCR struggles with low-quality scans, the engine prompts for a fallback process—either enhanced preprocessing or manual intervention.
Audit Trail & Logs: Every decision made by the engine is logged—providing a transparent audit trail of how values were extracted and validated.

Because of these built-in checks, the system achieves over 99% field-level accuracy in production scenarios. So while no OCR engine is perfect in isolation, this one can absolutely be trusted in real-world invoice workflows—especially when backed by smart automation, validation, and exception handling.

4. Our OCR-Based Automation Solution

To solve these issues, we developed a robust OCR-powered invoice processing engine:

Used Tesseract OCR and EasyOCR for scalable document digitization
Designed a custom entity data extraction engine using regex patterns, spatial layout recognition, and Machine Learning-based key-value extraction
Preprocessing images using OpenCV: binarization, skew correction, noise removal for better OCR accuracy
Defined validation logic to ensure mandatory fields like Invoice Number, Vendor, Amount, and Due Date were correctly captured
Integrated a pipeline to push structured data into Sage Accounting via API.

4a. Tesseract OCR: The Core of Our Extraction Engine

At the heart of our invoice automation system lies Tesseract OCR, an open-source OCR engine originally developed by Hewlett-Packard and now maintained by Google. It’s one of the most reliable and widely-used OCR tools in the industry, known for its accuracy, flexibility, and multilingual support.

We chose Tesseract OCR because it offers:

Strong Character Recognition: It performs exceptionally well on printed and scanned documents, which makes it perfect for reading structured invoice data like invoice numbers, dates, amounts, and vendor names.
Support for 100+ Languages: It’s capable of recognizing text in various languages, making it adaptable for international invoices.
Custom Training & Fine-Tuning: Tesseract allows us to train the engine with custom data if needed—this means we can improve accuracy for company-specific invoice formats.
Easy Integration with Python & OpenCV: Tesseract works seamlessly with our preprocessing pipeline (e.g., noise removal, grayscale filtering, binarization) for enhanced OCR results.
Box-Level Output: It provides bounding boxes for every word, which helps us map the spatial structure of the invoice for more precise data extraction.

In our use case, Tesseract OCR acts as the first step in the extraction pipeline, turning raw invoice images into machine-readable text. Its flexibility and high accuracy make it a powerful choice for building custom Intelligent Document Processing solutions like this one.

Official Sources

GitHub Repository: https://github.com/tesseract-ocr/tesseract
Official Documentation: https://tesseract-ocr.github.io/

4b. EasyOCR: A Modern Deep Learning-Based OCR Alternative

In addition to Tesseract OCR, we also incorporated EasyOCR, a Python-based OCR library that leverages Deep Learning models to recognize text in images with high accuracy, especially in complex or noisy documents.

We introduced EasyOCR into our pipeline for several key advantages:

Deep Learning-Powered Recognition: Unlike traditional OCR engines, EasyOCR uses convolutional Neural Networks (CNNs) and LSTM models to better understand distorted, rotated, or handwritten text.
Superior Accuracy on Low-Quality Scans: It performs significantly better on images with shadows, wrinkles, or unusual fonts—making it ideal for invoices captured via mobile phones or poorly scanned PDFs.
Multi-language Support: Like Tesseract, it supports over 80 languages, with advanced model support for complex scripts.
Out-of-the-Box Use: EasyOCR is easy to set up and deploy, requiring no custom training for standard invoice layouts.
Line and Paragraph Detection: It maintains the contextual structure of sentences, which helps when invoices are in paragraph-style formatting rather than tabular layouts.

We often use EasyOCR as a fallback or parallel engine when Tesseract OCR struggles with accuracy. This hybrid approach ensures higher reliability and coverage across diverse invoice types.

Official Sources

GitHub Repository: https://github.com/JaidedAI/EasyOCR
PyPI: https://pypi.org/project/easyocr/

4c. Tesseract OCR vs EasyOCR: A Comparison

Feature	Tesseract OCR	Easy OCR
Technology Base	Traditional OCR engine	Deep learning-based OCR engine (CNN + LSTM)
Accuracy	High for clean, printed text	Higher for noisy, skewed, or handwritten text
Preprocessing Required	Requires more preprocessing for best results	Performs well even with minimal preprocessing
Speed	Generally faster	Slightly slower due to neural network computations
Language Support	100+ languages	80+ languages
Customization	Supports custom training (complex setup)	Limited training support, but high out-of-box accuracy
Ease of Integration	CLI & Python APIs available	Python-based, very easy to use

4d. Regex and Pattern-Based Extraction

While OCR engines like Tesseract OCR and EasyOCR convert images into text, extracting meaningful data from that raw text requires another layer of intelligence. That’s where regex (regular expressions) and pattern-based logic come in.

What Is Regex?
Regex is a powerful tool used to search, match, and extract patterns in text—especially useful when dealing with structured fields like:

Invoice numbers
Dates
Tax IDs
Currency values

Regex helps automate this by scanning the OCR output for specific text patterns, allowing us to isolate key entities accurately and consistently.

Examples:

r’\bINV[- ]?\d{3,6}\b’ – INV-123456, INV1234
r’\b\d{2}/\d{2}/\d{4}\b’ – 24/03/2023, 01/12/2022

Why It Matters
OCR output can be messy, especially when multiple vendors use different invoice formats. Regex allows us to standardize and normalize this data by:

Automatically locating and validating fields
Reducing dependency on fixed templates
Increasing confidence in data extraction
Supporting multi-format invoices with minimal code changes

5. Integration with Sage Accounting

Real-time syncing of structured invoice data to Sage
Automated record creation for Vendors, Expenses, and Payment Schedules
Mapped extracted fields directly with Sage input schema
Implemented fallback logic for exception handling and manual review when confidence was low
Seamless API integration for direct posting into finance records

6. Results: From Bottlenecks to Breakthroughs

The implementation of OCR-driven invoice automation—powered by Tesseract OCR, EasyOCR, and regex-based pattern matching—delivered significant operational benefits.What was once a time-consuming and error-prone manual task became a streamlined, scalable pipeline.

Key Results:

80% reduction in manual data entry time
Over 99% field-level accuracy in processed invoices after validation logic
10,000+ invoices successfully processed in the first year of deployment
Invoice turnaround time reduced from 2–3 days to under 12 hours
Less than 1% of transactions required manual intervention
Improved data quality and audit readiness in Sage Accounting

7. Final Thoughts: Building the Foundation for Smart Finance

This project wasn’t just about reducing manual effort—it was about laying the groundwork for Intelligent Document Processing in finance.

By integrating OCR, deep learning, smart data extraction, and automated system API integration, we moved from manual chaos to a digital-first, audit-friendly, and scalable solution.

The success of this system opens up new opportunities, such as: