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Executive Summary

Traditional Optical Character Recognition OCR solutions often struggle with the complexities of real-world documents, leading to extraction errors, manual rework, and inflated costs. Focaloid Technologies offers an innovative approach that combines the strengths of open-source OCR, cloud-based OCR services, and advanced Large Language Models LLMs) to deliver superior accuracy, adaptability, and continuous improvement – all while optimizing for cost efficiency.

This solution is particularly valuable for small and medium-sized enterprises and startups seeking to streamline document-heavy processes. It delivers:

  • Enhanced Accuracy - LLMs learn from your specific documents, ensuring high-quality results even with layout variations and less structured formats.
  • Process Automation - Context-rich output powers accurate data entry, classification, and smarter downstream actions.
  • Cost-Effectiveness - A tiered OCR strategy and confidence-based quality gates deliver optimal ROI.
  • Scalability - Cloud integration allows easy scaling to match business growth

Introduction

The explosion of data in digital form poses a unique challenge for businesses: how to efficiently process and leverage this data for competitive advantage. Traditional OCR technologies have made strides in extracting text from images and documents, but Legacy OCR often falls short when encountering:

  • Complex or Irregular Layouts - Rigid templates fail with unusual invoices, forms, or handwritten documents.
  • Low-Quality Scans - Errors increase when dealing with poor resolution or faded text.
  • The Need for Adaptability - New document types or changed layouts require time-consuming reconfiguration.

Integrating these technologies with the latest advancements in LLMs, such as OpenAI’s models, propels this capability into a new realm of efficiency and effectiveness.

This white paper outlines Focaloid Technologies’ approach to harnessing these technologies, providing a roadmap for SMEs and startups looking to streamline their document-heavy processes.

Solution

OCR + AI + Intelligent Orchestration

At the heart of our innovative document processing solution lies the seamless integration of Optical Character Recognition OCR technology with the advanced capabilities of Large Language Models (LLMs). This integration is not merely about enhancing accuracy but also about harnessing the power of continuous learning to adapt and improve over time. Our approach leverages the unique few-shot learning abilities of LLMs, setting our solution apart from traditional machine learning algorithms.

Technical Components

  • Adaptive OCR Processing

    The cornerstone of our solution is a multi-tiered OCR processing strategy. Initially, documents are scanned using an open-source OCR engine, providing a cost-effective method for converting images of text into machine-encoded text. This stage is designed to maximize efficiency, processing a wide array of documents with high accuracy.

  • Confidence Score Evaluation

    Each document scan is accompanied by a confidence score, a metric that assesses the accuracy of the OCR output. This score is pivotal in ensuring the reliability of the data extracted. Documents that meet or exceed a predefined confidence threshold are accepted for further processing, while those that fall short are subjected to a secondary scan.

  • Secondary OCR Processing

    Documents that do not meet the initial confidence criteria are processed through a more sophisticated cloud-based OCR engine, such as Amazon Textract, Azure AI Vision, or Google Cloud Vision. This step underscores our commitment to accuracy, employing advanced technology to salvage data from documents that pose a challenge to less capable engines.

  • LLM Enhanced Contextualization

    The integration of LLMs with OCR technologies not only enhances efficiency and accuracy but also introduces a dynamic capability for continuous improvement. Leveraging few-shot learning, LLMs quickly adapt to new data patterns and contexts, significantly reducing the time and resources required for training. This continuous improvement cycle ensures our solution evolves over time, becoming increasingly effective at processing a diverse range of documents with minimal human intervention.

  • Continuous Learning Through LLMs Few-Shot Learning for Rapid Adaptation

    The integration of LLMs with OCR technologies not only enhances efficiency and accuracy but also introduces a dynamic capability for continuous improvement. Leveraging few-shot learning, LLMs quickly adapt to new data patterns and contexts, significantly reducing the time and resources required for training. This continuous improvement cycle ensures our solution evolves over time, becoming increasingly effective at processing a diverse range of documents with minimal human intervention.

The Focaloid Advantage

Focaloid Technologies prides itself on a deep understanding of both OCR engines and LLMs. Our team’s ability to fine-tune processes, adjust confidence score thresholds, and choose the most suitable technology stack for each project positions us as a leader in digital transformation solutions.

  • Domain Expertise - Deep understanding of OCR integration and business process optimization.
  • Data-Driven Customization - Tailored solutions based on your unique document sets
  • Continuous Improvement - Managed teams monitor accuracy and adapt models as needed.
  • Client-Centric - Focus on delivering ROI and meeting your evolving needs.

Technology Landscape

  • DocTR (Document Text Recognition) - Versatile for various document types, good for forms and tables. Supports pre-trained models and multiple languages. Read More
  • Tesseract - One of the most established OCR engines, highly customizable. Strong language support but can be harder to configure optimally. Read More
  • Amazon Textract - High accuracy, handles complex layouts well. Seamless integration with other AWS services and pay-per-use pricing. Read More
  • Google Document AI - Specialized pre-trained models for invoices, receipts, etc. Excels with structured documents but has a per-page pricing model. Read More
  • Microsoft Azure OCR - Part of Azure Cognitive Services. Good language support, offers form recognition features. Read More

Large Language Models (LLMs)

  • OpenAI Models (GPT 3 series) - These models offer exceptional power and adaptability. When fine-tuned with a sample of your document data, they deliver highly accurate results. OpenAI provides API access with various pricing tiers based on the chosen model size and the volume of your requests.
  • Meta LLaMA - These models are known for their strong performance considering their smaller size. As an open-source option, LLaMA provides a cost-effective path for custom training and avoids the recurring costs of commercial APIs. Available in multiple sizes(7B, 13B, 33B, 65B parameters), LLaMA allows you to find the right balance between performance and resource requirements.

Usecase

Streamlining Invoice Processing

Technology Landscape
  • Invoice Number
  • Due Date
  • Vendor
  • Line Items
Benefits
  • Reduced errors
  • Freed staff hours from manual entry
  • Accelerated payment processing
  • Improved financial data integrity