Efficient Data Extraction from Physical Documents with ML
Problem
Many companies face a common challenge: manually copying data from physical documents into digital software. This time-consuming and error-prone process requires substantial time, resources, and financial investments to maintain dedicated departments.
Solution
To address this challenge, we developed a platform using machine learning. This solution was designed to receive scanned documents and autonomously extract the relevant information from them. The extracted data is then integrated into specific tables within the client’s system, automating the data entry process and saving businesses time and resources.
Result
To address this challenge, we developed a platform using machine learning. This solution was designed to receive scanned documents and autonomously extract the relevant information from them. The extracted data is then integrated into specific tables within the client’s system, automating the data entry process and saving businesses time and resources.
Accomplishments
- Time-Saving: Automating data extraction saves time and frees up employees for more strategic work.
- Resource Optimization: Eliminating manual data entry also saves resources and money.
- Data Quality: The machine learning model provides consistent and accurate data entry, minimizing errors and improving data quality.
