Understanding documents is extremely valuable when setting up a successful AI-powered OCR solution because not all OCR solutions are created equal. OCR technology extracts text and information from images or scanned documents, and its effectiveness depends on understanding the quality and relevance of the documents being processed, which isn’t a part of lone OCR technology. When OCR is paired with AI and ML creates a machine that “understands” your documents more than OCR alone.
OCR Features
Document Variability
Documents can vary widely in terms of layout, fonts, languages, and formatting. OCR on its own struggles to understand documents that aren’t formatted in a very specific way. Understanding the types of documents you will be processing helps in customizing the OCR system to handle these variations effectively.
Preprocessing
Knowing the documents allows you to perform preprocessing tasks such as image enhancement, noise reduction, and deskewing, which can significantly improve OCR accuracy. Different document types may require different preprocessing steps. Once you know the different types, your model can be trained on what to look for.
Language and Script Recognition
If your documents contain multiple languages or scripts, understanding the document content helps in configuring the OCR to recognize and process each language correctly. This is helpful for handwriting or other forms of blurriness that can cause documents to be hard to read.
Layout Analysis
Understanding the document structure and layout enables the OCR system to identify headers, footers, tables, and other structural elements. This is crucial for preserving the document’s semantic meaning during OCR.
Field Extraction
In the case of AP automation, you do not need to extract text from the entire document but only specific fields or regions (e.g., extracting customer names, dates, and amounts from invoices). Understanding the document layout helps in defining these regions for extraction.
Error Handling
Recognizing common errors or variations in the documents allows you to implement error-handling mechanisms, such as verifying extracted data against predefined patterns or rules. It also helps you to define the path that errors take if or when they come up in processing.
Training and Tuning
Training and fine-tuning the OCR model often require labeled data for supervised learning. Understanding the documents helps in creating training datasets that reflect the real-world variations present in your document collection.
Post-Processing
After OCR, you may need to perform post-processing tasks, such as data validation or entity recognition. Knowing the context of the documents aids in designing effective post-processing routines.
Performance Metrics
Understanding the documents helps in setting realistic performance metrics for your OCR solution. Different types of documents may have varying levels of difficulty, and you need to assess OCR accuracy accordingly.
Scalability and Maintenance
Knowing the document types and potential future changes in document formats allows you to design a scalable and maintainable AI-assisted OCR solution. You can plan for updates and improvements based on your understanding of the documents.
What does a successful OCR solution look like?
Many companies are disappointed when the promises of OCR fail to deliver a return on investment. This often occurs because accuracy rates are too low. Outputting bad data can result in paying vendors the wrong amount or even paying the wrong vendors altogether! Additionally, the high costs of post-processing clean-up make it clear why there is a lack of ROI. However, the technology isn’t the problem; it’s the process.
For a successful AI-assisted OCR program to deliver an acceptable ROI, the first step must be a thorough examination of the target documents, process, and expected results. Utilizing “real-world” documents (not random test documents), a series of tests can provide data on expected results, giving decision-makers the information needed to determine if the investment will provide the ROI needed. In summary, understanding the documents and process you’re working with is fundamental to the success of your AI OCR solution.
Conclusion
With AI-assisted technology, the tools are in place, but can they deliver the savings to justify the investment? Since not all OCR solutions are created equal, be sure to choose a solution provider that helps you do your research and understand your documents. Let your solution partner help you tailor the AI-assisted OCR system to the specific needs of your document collection, resulting in higher accuracy and efficiency in text extraction and data processing.
Contact ICG to start a conversation on how ICG’s AI-assisted data capture solutions can deliver immediate value to your organization. ICG will assist you in testing your documents and determining what results you might expect. You can also request a demo of one of our data capture or comprehensive cloud-hosted AP automation solutions and see for yourself how your company can take advantage of the power of AI. For a quick view of ICG’s solutions, view this short video.