Case study
Automated Placeholder Document Creation for Digitization of Pharma Templates
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.
Table of contents
Business objective
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
Reduced Manual Effort
Reduced manual intervention, freeing up human resources for higher-value tasks.
More results
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