When it comes to choosing OCR (Optical Character Recognition) technology, developers have a lot to consider. Since OCR solutions have been around for decades, it’s tempting to think that they are standardized and thus, any of them will do. That couldn’t be farther from the truth: not all OCRs are created equally, so choosing the right one can still be a headache. From the type of models to AI offerings to pricing and community support, many factors play a crucial role in determining the best fit for your project. This article covers key points to keep in mind, including considerations for open source models, limitations of LLMs, and pricing.
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Open-source models: Cost effective, but less accurate
Open-source OCR models like Tesseract and PaddleOCR are popular choices among developers due to their accessibility and cost-effectiveness. However, they come with certain limitations:
- Accuracy: Open-source models often have lower accuracy compared to commercial engines. They struggle with handwriting, rotated text, and low-quality images.
- Support for complex documents: These models may not handle complex documents, tables, and charts effectively.
- Continuous optimization: Enhancements to OSS models are at the whim of the community. Maintainers come and go, and their priorities often differ from your project’s needs. Proprietary companies maintain an edge through continuous optimization, leveraging years of practical experience and refined technologies.
Open-source OCR models may work for POCs or processing simple documents, but if high-quality, reliable accuracy is a must, they are a no-go.
Can LLMs replace OCR? Not so fast
LLMs like GPT-4.5 and other general-purpose AI models are increasingly being used for document processing. The ability to quickly test their OCR abilities by uploading a document through a web UI or chatbot is compelling. However, they also have their challenges:
- Hallucinations: LLMs often omit significant portions of text, hallucinate content, and fail to output text coordinates.
- Inconsistencies: They display inconsistent formatting and table extraction, making them less reliable for robust OCR tasks. Results themselves are inconsistent too, meaning you could process the same document ten times and get ten different results.
- Speed and cost: LLM-based extraction can be slow and expensive due to high compute costs.
Due to the unpredictability of inaccuracies in large language models (LLMs), the automation of business processes is hindered. This puts significant burden on the developer to capture errors and code exceptions, feeling like a game of “LLM whack-a-mole.” Downstream, any issues missed would require users to resort to manual corrections. This defeats the purpose of introducing OCR solutions in the first place.
Pricing: Cheap may cost you more
Pricing is a critical factor when choosing an OCR solution, but it's not just about the cost.
- Support and reliability: A significant benefit of paying for a solution, especially when business-critical processes depend on it, is ready access to the support, advisory, and SLAs are included.
- Cost-effectiveness: Look for solutions that offer a low-cost, pay-as-you-go model, ensuring scalable solutions without unexpected expenses.
- Free trials and freemium tiers: Many commercial OCR solutions offer free trials or freemium tiers, allowing developers to test capabilities before committing.
- Capability comparisons: Many solutions, especially those from hyperscalers like Microsoft or AWS, appear cheap up front because they price their OCR capabilities a la carte. When compared to an all-inclusive pricing model, of course it’ll seem cheaper! Review all pricing pages carefully.
When assessing OCR solutions, seek those that provide adequate trial periods, sufficient document processing capacity, and a pay-as-you-go pricing model.
Developer support and community
A great product is not enough; comprehensive support and an active community are essential.
- Documentation and SDKs: Ensure the OCR solution provides detailed documentation, SDKs, and sandbox environments to streamline integration and optimize solutions.
- Community engagement: The OCR solution should have an active and friendly developer community to turn to if needed. The best encourage you to exchange ideas, get expert guidance, and enhance your OCR implementations.
The OCR world is more complex than it looks on the surface. It’s a solved problem, until you need real-world accuracy, reliability, and robust capabilities. To ensure project success, look for a strong company and community-backed solution.
Introducing ABBYY’s purpose-built document OCR API for developers (coming soon)
Choosing the right OCR solution involves balancing the above factors to meet your specific needs. If your project is business critical, then ABBYY’s new Document AI platform warrants a look.
ABBYY’s upcoming Document AI API is a developer-friendly, purpose-built OCR service designed for seamless integration into AI-powered business process automation workflows. It efficiently converts unstructured business documents into structured JSON with exceptional accuracy and reliability, equipping your business solutions and application for success.
