Data Extraction - Clustrex Data

Extract Information from Images and PDF

Despite living in the digital age, we still have a strong presence of financial documents such as invoices in paper form. Clustrex Document Extraction services reduce time, effort taken to get the information into structured form, minimizing this tiresome data entry. With our service, you can process hundreds or even thousands of invoices within minutes thereby reducing cost, improving accuracy and increasing productivity. API allows for processing of documents in bulk and getting the extracted info in structured formats that can be consumed by your existing workflow. We also provide complete customizable outputs to automate your existing workflow including human review.

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Healthcare Document Parser

Medical records such as new patient registration, insurance claim form have lot of data that need to be extracted. Manual data entry is slow and error prone. OCR tools are not flexible enough. Welcome to the Clustrex Medical Record Parser !

This tools extracts data that can be consumed by any application for verification and further processing in the workflow. API allows for processing records in bulk and also integration with other applications or data flow. This saves time, effort, cost, and greatly increases efficiency of the operations.

Resume Parser

Its hard to parse and filter data from applicant resumes for a job post, when the responses are high. Clustrex provides the best in class resume parser tool, that extracts key fields like name, contact, education, skills, experience and more from the resume. API allows for processing resumes in bulk and get the extracted info in structured formats that can be consumed by HR professionals or integrated with other applications.

Case Study

Generalized Document Extraction Using JSON Template with
Intelligent AI-driven document processing algorithms

Background:
Extracting structured data from documents such as PDFs, Word files, and images is a complex task due to variations in layouts and formats. Traditional rule-based and OCR-based approaches struggle with flexibility and adaptability across different document types.
Challenges:
A solution was required that could:
- Extract relevant data fields dynamically based on a given JSON template.
- Work across diverse document types, such as invoices, resumes, legal documents, financial reports, and more.
- Maintain high accuracy while handling unstructured content
- Efficiently extract data from handwritten documents.
- Ensure the extracted data is structured correctly in the specified JSON format.
Solution:
To address these challenges, a generalized document extraction pipeline leveraging intelligent AI-driven document processing algorithms was developed. The solution consists of the following components:
- 1. Document Preprocessing:
  - Filtered required pages or regions from the given document.
  - Applied image processing with OpenCV.
  - Processed Word documents using python-docx.
- 2. Intelligent Data Extraction and JSON Mapping:
  - Analyzed raw document content to extract key fields based on a given JSON template.
  - Utilized AI-driven algorithms to interpret and structure the extracted data.
  - Mapped extracted values dynamically to user-defined JSON keys without requiring custom coding.
  - Ensured that the JSON structure adhered to expected formatting, including verifying key-value pairs, handling missing data, and correcting formatting inconsistencies.
- 3. Dynamic JSON Template Processing:
  - Accepted any user-defined JSON template to specify required fields.
  - Adapted to varying document structures by intelligently identifying corresponding sections.
  - Allowed modification and extension of JSON templates to accommodate different document types and data fields.
- 4. Post-processing and Validation:
  - Verified the integrity of the JSON output to ensure completeness and correct formatting.
  - Applied business rules and logic-based checks to validate extracted data.
  - Flagged missing or inconsistent data for manual review, ensuring high accuracy.
  - Checked for proper JSON formatting to prevent missing or malformed structures (e.g., ensuring curly braces are correctly paired).
Results:
The implementation led to significant improvements:
- Flexibility: Worked across multiple document types (PDFs, Word, and images) without requiring format-specific rules.
- Accuracy: Achieved over 95% accuracy in structured data extraction and JSON mapping.
- Efficiency: Reduced manual data entry efforts by automating extraction for varied templates.
- Scalability: Enabled seamless integration into various document processing workflows, accommodating new document types with minimal adjustments.
Conclusion:
By implementing structured document extraction using a user-defined JSON template, the solution provides a flexible and scalable approach applicable to multiple domains. This automation reduces dependency on custom scripts for each document type while ensuring consistent data integrity.
Future Scope:
- Enhancing AI model performance with fine-tuning on domain-specific datasets.
- Expanding the solution to support multilingual document extraction.
- Integrating with enterprise RPA tools for end-to-end automation.
- Improving error detection and self-correction mechanisms for JSON validation and structure refinement.

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