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How AI Document Processing Works,5 Steps in Automated Document Processing

In today’s digital age, where businesses are racing to embrace the future, manual document processing may seem like a horse-drawn carriage on a superhighway. Surprisingly, despite the widespread adoption of advanced technologies, companies still haven’t shifted to AI in document processing. Around 80% of the data generated by organizations remains in an unstructured state in various formats, similar to scattered puzzle pieces —spreadsheets, PDFs, images—each demanding its own unique handling.

However, setting up AI document processing for your organization is NOT a difficult proposition. This is because Products/Tools like DocExtract from iTech are already in the market and can be further customized to meet individual business needs.

For an inside understanding of how AI document processing works, we will take you through the steps involved.

Step by Step Guide to how AI document processing works and AI technologies involved

There are 5 distinct steps involved in automated document processing

1. Document Intake

This step involves receiving documents through various channels, such as email, file uploads, or APIs. The technology used for data ingestion can include web scraping tools, email parsers, or custom integration code to fetch documents from external sources.

In the case of physical documents, they may be scanned or photographed to create digital copies.

2. Document Type Identification

To identify the type of document (e.g., invoice, medical forms, purchase form, legal contract, etc.), the system typically employs a combination of techniques, including:

Rule-Based Classification and Metadata Analysis:

  • Regex (Regular Expressions): Identifies documents based on specific keywords or phrases, such as “Invoice” or file metadata like extensions.
  • File Metadata Analysis: Uses document metadata, such as file extensions, for initial classification.

Machine Learning Classification:

  • Machine Learning Models: Trains models (e.g., SVM, Random Forests, or deep learning) on labeled datasets to classify documents based on content.
  • NLP Techniques: Analyzes textual content using NLP to classify documents based on keywords, phrases, or linguistic patterns.

Image-Based and Layout Analysis:

  • Image Analysis (Computer Vision): Detects visual elements (e.g., logos, headers) in image-based documents using computer vision techniques.
  • Layout Analysis Algorithms: Identifies form fields, labels, and relationships in structured documents, employing techniques like OMR and OCR.

3. Text Block Detection:

OCR technology, such as Tesseract, is used to recognize and extract text from scanned documents or images. OCR software identifies characters and their positions within the document.

  • Object detection models like YOLO (You Only Look Once) or Faster R-CNN can identify and classify non-textual elements like images, logos, or graphs within documents.
  • Table Recognition Models: Machine learning models or rule-based algorithms can identify and extract tabular structures from documents by recognizing patterns in cell layouts and borders.
  • Layout Analysis Algorithms: These algorithms can identify text regions, headings, paragraphs, and other textual elements by analyzing the spatial distribution of characters.

Step 4. Feature Extraction

  • Tokenization – Breaking down text into individual words or tokens.
  • Font Styles and Sizes – Extracting information about font styles, sizes, and formatting.
  • Coordinates – Capturing the spatial coordinates of text blocks, which helps in preserving
  • Capturing metadata about images, such as resolution, file format, and dimensions.
  • Using image analysis techniques to describe the content or objects within images.
  • Named Entity Recognition (NER) – NER models can identify and extract metadata such as dates, names, addresses, and other specific entities from the text.

Step 5. Document Routing

Workflow Orchestration: Workflow management systems or business process automation tools can be used to route documents to the appropriate processing pipelines based on their classification. This often involves integration with other systems and APIs.

The technology stack used in each step can vary based on the specific requirements of the document processing system and the types of documents being processed. Commercial document processing platforms and open-source libraries often provide pre-built components and APIs for these tasks, making it easier to implement a document processing solution.

More about the technology involved in Document data Extraction and Document Processing

Intelligent document processing uses varying AI technologies based on the requirements.

Intelligent document processing refers to the use of artificial intelligence (AI) and machine learning (ML) technologies to extract, analyze, and manage information from documents in a way that mimics human intelligence.

How Optical Character Recognition (OCR) works within Document Processing

Preprocessing: In document processing, OCR technology is used to convert scanned documents or images into machine-readable text. It begins with preprocessing, where the document image is enhanced to improve OCR accuracy. This may involve tasks such as noise reduction, contrast adjustment, and skew correction.

To separate text from the background, OCR systems binarize the image, turning it into black and white. This simplifies character recognition.

The document is segmented into smaller regions, such as lines or words. OCR algorithms identify these regions to analyze them individually.

From these regions, OCR systems extract features like character shapes, sizes, and patterns. These features are crucial for character recognition.

Machine learning algorithms, often based on neural networks or pattern recognition models, are employed to classify these extracted features into specific characters. Neural networks, for example, can learn intricate patterns and variations in character shapes.

Automated document processing models use character recognition to refine and organize texts into words and sentences. Post-processing may involve error correction and language modeling to improve the overall accuracy of the OCR results.

Named Entity Recognition (NER) in AI Document Processing

NER models used in AI document processing are typically trained on labeled datasets where entities like names of people, places, and organizations are annotated. During training, the model learns to recognize patterns, context, and linguistic features associated with these named entities.

Within the document, the text is tokenized, meaning it is split into individual words or phrases, which are then processed individually by the NER model.

The NER model assigns labels to each token, indicating whether it represents a named entity and, if so, what type of entity it is (e.g., person, location, organization). This is achieved through probabilistic modeling and considering the context of each token.

NER models analyze the context of each token to improve accuracy. For instance, they differentiate between “Apple” as an organization in “Apple Inc.” and “apple” as a fruit in “apple pie” based on the surrounding words and context.

Faster R-CNN (Region-based Convolutional Neural Network)

How it works within automated document processing

Region Proposal Network (RPN): In document processing applications, Faster R-CNN can be used for object detection, such as locating and recognizing specific document elements. The RPN part of the network generates potential bounding box regions in the document image that might contain these elements. RPN uses convolutional layers to predict these regions based on feature maps from the deep convolutional neural network.

A deep convolutional neural network (e.g., ResNet or VGG) is employed to extract features from the document image. These features capture relevant information about the document’s content, structure, and the objects within it.

Once regions are proposed, they are classified into specific categories (e.g., headings, paragraphs, tables) using a classification head. This step involves determining what type of document element each proposed region represents.

Bounding boxes around recognized document elements are refined to improve their accuracy and alignment with detected objects. This refinement ensures that the bounding boxes closely match the actual positions of document elements.

A key factor for business owners is selecting a trustworthy development partner with expertise in aligning technology with business requirements. With experienced professionals, you can smoothly implement intelligent document processing and reap the benefits of AI-driven process optimization. Talk to our experts.

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