CASE STUDY Detail

Automated Placeholder Document Creation for Digitization of Pharma Templates

Industry
Pharmaceuticals
Technologies
NLP (Natural Language Processing)
capabilites
AI and Advanced Analytics

Business Impact

95% Reduction in Manual Effort

90–95% Accuracy in Placeholder Text Replacement

Drastic Time Reduction

Significant Cost Savings

Table of Contents

Business Objective / Goal

To automate and digitize the process of updating medicine preparation templates by replacing placeholder text with meaningful chemical and process information—eliminating manual effort, reducing human error, and speeding up processing across large volumes of documents.

Solutions & Implementation

  • Developed a custom Named Entity Recognition (NER) solution using both POS tagging and LSTM-based deep learning models to identify contextual entities around placeholders.
  • Implemented logic to scan for meaningful text around placeholder tags (marked by ##) and narrowed down the text window for more accurate entity prediction.
  • Replaced blank sections in .odt templates with relevant chemical names or phrases without losing sentence semantics.
  • Designed the workflow to handle both individual documents and folders containing multiple templates.
  • Deployed the solution in Rapidminer for seamless execution and batch processing.

Major Technologies Used

  • Python – Core scripting and data processing
  • Keras – LSTM-based deep learning model development
  • Gensim – Semantic text processing
  • Powershell Scripts – Document parsing and batch execution
  • Rapidminer – Workflow orchestration and user interface for business users

Business Outcomes

  • 95% Reduction in Manual Effort 95% reduction in manual intervention, freeing up human resources for higher-value tasks.

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