Pre-Processing
The first step in intelligent document processing is pre-processing. This step involves binarization, noise reduction, de-skewing, and de-speckling. These techniques help to improve the quality of the document images before they are processed by OCR and AI algorithms. This ensures that the data extracted is as accurate as possible, minimizing errors in downstream processes.
Intelligent Document Classification
The next step is intelligent document classification. This step involves NLP, unsupervised and supervised learning, OCR, and Google Vision to classify documents based on their type and content. This allows for more efficient routing of documents to the appropriate processing workflows. To decipher difficult content, intelligent character recognition (ICR) takes OCR to the next level, applying AI to better identify glyphs and other textual elements that are difficult to read.
Data Extraction
The third step is data extraction, where AI algorithms are used to extract relevant data from the classified documents. This can include text, numeric values, and even images or signatures. Extraction employs NLP, deep learning, machine learning, OCR, and Google Vision.
Domain Specific Validation
The fourth step is domain-specific validation, accomplished by applying fuzzy logic, regular expression (RegEx), rules, and scripts to assess, match, and manage the extracted data for accuracy and relevance to the specific industry or business context. Additionally, enhanced validation with robotic process automation (RPA) can further verify the extracted data for suitability to the prescribed purpose or process.
Human-in-the-Loop Validation
Human-in-the-loop (HITL) validation is another component of IDP that increases the quality of automated data processing. HITL validation uses supervised learning to provide a rapid feedback loop and fine-tune AI training by correcting data via human input.
