Project Info
Client
IQVIA
Service
WEB Development
Industry
Retail, eCommerce
Stack
Android, Realm, Dagger 2, RxJava
Challenge
Creating a document parser presents several challenges, primarily stemming from the complexity and variability of document formats, structures, and content. Here are some of the key challenges: Document Variability: Documents come in various formats such as PDFs, Word documents, scanned images, and emails, each with its own structure and layout.
Unstructured Data: Many documents contain unstructured or semi-structured data, making it challenging to extract relevant information accurately. Unstructured data can include free-form text, tables, images, and graphs, requiring advanced techniques such as Natural Language Processing and OCR to extract and interpret information accurately.
Document Complexity: Documents can vary in complexity, ranging from simple text documents to highly structured reports with nested data,tables, and charts.
Data Extraction Accuracy: Ensuring the accuracy of data extraction is crucial for the reliability of the document parser. Inaccurate extraction of information can lead to errors in analysis and decision-making.
Scalability and Performance: Processing large volumes of documents efficiently while maintaining high accuracy and performance is a significant challenge for document parsers.
Adaptability to Changes: Documents and document formats evolve over time, requiring the parser to adapt to changes in document structure, content, and formatting. Keeping the parser up-to-date with the latest document formats and standards is essential to maintain accuracy and reliability.
Error Handling and Robustness: Document parsing is inherently error-prone due to factors such as noise, variability, and ambiguity in document content. Robust error handling mechanisms are needed to detect and recover from parsing errors gracefully, ensuring the reliability and robustness of the parser.
Overcoming these challenges requires a combination of advanced technologies, including machine learning, NLP, OCR, and computer vision, coupled with robust software engineering practices and domain-specific expertise. Building a fully dynamic document parser requires careful planning, experimentation, and iterative refinement to create a solution that meets the specific needs and requirements of the target application.
Our Solution
Based on the investigation findings, we proposed the development of a customized document parser tool designed to address the client's specific needs and challenges. The proposed solution included the following key features:
Natural Language Processing (NLP) Capabilities: Advanced NLP algorithms were employed to extract relevant information from unstructured text in scientific literature, patents, and regulatory documents.
Customizable Templates: The document parser tool would support customizable templates for data extraction, allowing researchers to define specific data points and criteria for extraction based on their research objectives.
Integration with Existing Systems: Integration with existing data management systems and databases using the API interface of the existing solution was prepared on platform.
Quality Assurance Mechanisms: Robust quality assurance mechanisms, including validation checks and error handling procedures, were implemented to ensure the accuracy and reliability of extracted data.
Multi File Types: a document parser tool that can read and extract data from PDFs, Word docs, and other common document formats. This tool can be used to create reports, populate databases, and more. Our document parser tool is easy to use and can be customized to your specific needs. Contact us today to learn more about our document parser tool and how it can help you streamline your workflows.
The initial documents were collected for the pattern analysis and the common patterns were identified. The documents were classified into recurring and non recurring data lines.
Custom parser was used to read a contents of the document and the data is returned in text format.
The Departments Benefitted
The document parser tool accelerates drug discovery by efficiently extracting data from scientific literature and patents. It aids regulatory affairs in analyzing compliance documents and ensures adherence to regulations. Additionally, it supports intellectual property teams by extracting key insights from patents, fostering opportunities for filings and protection.
Research and Development
Regulatory Affairs
Intellectual Property
The Impact
The document parser tool delivered a 300% increase in data collection speed, significantly enhancing efficiency by reducing time and resource demands. Advanced NLP algorithms ensured high data accuracy, minimizing errors. Researchers gained faster access to insights, boosting productivity and enabling informed decision-making to drive innovation in drug discovery
Efficiency Gains
Improved Data Accuracy
Enhanced Research Productivity
Conclusion
The development and implementation of the customized document parser tool addressed critical challenges in document variability, unstructured data, and scalability. By leveraging advanced NLP algorithms, robust quality assurance mechanisms, and customizable templates, the solution ensured accurate, efficient, and scalable data extraction. Integration with existing systems streamlined workflows, improving research productivity and decision-making. This dynamic tool demonstrates the potential to transform document processing in research, regulatory, and business environments, offering significant time and resource savings while enhancing data reliability and usability.
Technology Assessment
An assessment of existing document parsing technologies and tools highlighted the limitations of off-the-shelf solutions in meeting the client's specific requirements and customization needs. We initially prepared an MVP which was done using 2 top document formats which were highly complex. Which also consisted the tabular data.
Stakeholder Interviews
Through stakeholder interviews with key departments including research, regulatory affairs, and intellectual property, we identified common pain points related to data extraction and analysis by talking to the data collection team, including inefficiency, data fragmentation, and information overload. We also identified the exact outcome of the system.
Data Analysis
A comprehensive analysis of existing data extraction processes revealed bottlenecks and inefficiencies, indicating the need for a more automated and streamlined approach to data extraction. Also a in-depth study of all the documents were conducted. Majority of the data was in tabular format. We also studied the input data from various vendors and ERPs.