AI and SimpleIndex
When it comes to automating the document processing workflow, it is impossible to avoid the question of AI. It is very hard to do anything nowadays without some use of AI-powered tools. Recent studies have shown that at least 40% of the average workday will soon be done by AI.
AI is involved with OCR in two major ways:
- AI OCR training
- AI Document Classification
AI OCR training
AI OCR training algorithms use artificial intelligence to improve recognition accuracy and automatically identify common data elements based on learned context.
AI OCR training refers to two things:
- Tuning the OCR Engine to improve recognition of new fonts, languages, or handwritten text.
- Training data capture software to identify the correct location of fields on various related documents.
AI OCR training is an important process that enables Artificial Intelligence models to efficiently and correctly extract data from scanned documents, having many practical applications in a broad range of business fields.
Recent advances in AI allow our OCR system to perform at higher levels of accuracy and efficiency by collecting large amounts of data from scanned documents and using it to identify patterns, characters, words, and other elements of text. The more data, the better the performance and accuracy.
AI Document Classification
AI Classification of documents allows data capture applications to quickly determine what type of document is being processed before extracting data from the OCR text.
AI classification algorithms use text matching, page layouts, and artificial intelligence to train models that are able to identify documents by type even when the formatting and quality varies significantly. Overall AI-powered OCR tools use machine learning algorithms to automate processes, making it faster and more accurate than manual data entry.
While most data capture applications are able to identify document types based on recognition templates, automatic classification algorithms are much faster and significantly improve throughput when there are many different types of documents being processed. Trained AI classification models can also seem to “understand” the common traits of different document types and sort them correctly even when presented with new formats.
Pros and Cons, or when and what to use?
The use of AI in OCR and document processing has its strong and weak parts. It is also important to remember when and what AI is needed. You can learn more about it here.
But in short, here are the main parts.
When to use a traditional OCR? And AI disadvantages.
- Rule-based systems can handle structured data well enough
- High structure of documents to process means low ambiguity of data
- Cost and complexity is too high
- Document processing needs to work offline or in a strict security environment.
- Data processing precision and AI Hallucinations.
When to use AI in document capture? And AI advantages.
- Unstructured or Semi-Structured Documents
- High Variability in Layouts or Formats
- Necessity for classifying a large volume of different document types and formats
- Handwriting Document Recognition
- Necessity for understanding language and entities
- Noisy, Low-Quality Scans, or Images
- Changing volume and types of documents that require learning and adapting over time
- Data processing is already happening in a cloud environment
SimpleIndex combines the best features of AI-trained OCR engines (choice of Abbyy FineReader or Amazon Textract) and some AI document classification features while staying on the lower-cost and on-premise side of OCR.
Simple Software’s SimpleIndex application provides keyword and pattern matching based document classification at a much lower cost than enterprise solutions.
Our collection of OCR Data Capture applications all have built-in automatic document classification capabilities, including machine learning and script based manual overrides.
ChatGPT integration puts powerful AI document analysis functions into a custom Autofill lookup. This allows you to extract index values and text from any document and use them to create an AI prompt. The answer provided by ChatGPT can then be saved as an index field value or parsed into multiple values.
Currently, ChatGPT 3.5 Turbo is the only AI model available. Other models can be enabled by request. Please Contact Us to request additional models and other features.
You can also create your own AI integrations with a Custom Code Autofill function.
LoanStacker is an OCR application that is AI-trained to process documents in the field of mortgage loans and finances. It is a good example of an AI-based automatic classification application, which takes a complete residential mortgage loan file and identifies the more than 500 forms, disclosures, tax records, and contracts they contain. Once identified, these documents can be sent to the appropriate workflows for approval, data entry, etc.
