Intelligent Document Processing

The client faced challenges with inefficient manual data entry from diverse lab reports, resulting in inaccuracies and resource-intensive processes. Handling variability in report formats, accurately extracting handwritten data, and customizing solutions across domains were major pain points.

• Extract personal details (name, registration identifier, gender, age) from PDF lab reports
• Identify specific test information mentioned in the reports
• Implement image processing and OCR techniques for accurate data extraction
• Adapt to different report formats and support scalability
• Customize domain-specific layers to accommodate various healthcare domains and report types
• Recognize and extract handwritten data, even if it overlaps with printed text

The solution leveraged image processing and optical character recognition (OCR) to efficiently extract data from scanned PDF lab reports, overcoming limitations encountered by traditional parsing mechanisms. Its scalable architecture provided the flexibility to fine-tune operations, ensuring compatibility with diverse report formats. Integration of a pluggable domain-specific layer, enabled seamless customization across different domains, enabling the identification and extraction of relevant information based on specific use cases, thereby enhancing precision and relevance.

The solution employed image-based models to accurately extract handwritten data. Through sophisticated algorithms, it discerned handwritten text, even in varied orientations, overlapping with printed details, or existing within diverse document layouts. The solution features logic mechanisms to reject less accurate outputs, thereby significantly improving the precision and reliability of data extracted from handwritten sections.

The comprehensive approach not only streamlined the extraction process from scanned documents but also provided a robust framework for accommodating various forms, domains, and data types. Its adaptability and accuracy enhancements served as crucial components in automating data entry, reducing errors, and ultimately empowering the client with more efficient and accurate data analysis capabilities.

• Multi-source data extraction
• Robust image processing
• Domain-specific salesforce-customization
• Handwritten text recognition
• Predefined data coordinates support
• Adaptability and customization
• Error rejection logic

• Automated data extraction reduced the manual effort required for analyzing lab reports, improving overall operational efficiency.
• Implementation of domain-specific logic and rejection of inaccurate outputs increased the reliability of extracted data, reducing errors.
• By automating repetitive data entry tasks, the company saved significant time and resources, leading to cost savings.
• The customizable nature of the solution allowed it to adapt to various forms and domains within the healthcare industry, enhancing its versatility.

• OpenAI
• Java
• OCR
• Computer vision
• Python
• Google Cloud Platform