Artificial Intelligence Enhanced Optical Character Recognition
Case Overview.
Our client, a leading financial corporation, processes thousands of applications daily. This workflow required data to be manually digitized from physical documents, resulting in significant inefficiencies. The manual process was prone to errors and inaccuracies, leading to an increase in operational time and costs. While existing OCR (Optical Character Recognition) technologies had been implemented to automate parts of the process, they failed to meet the precision and efficiency requirements due to limitations like recognition errors and poor image quality handling. The client needed an advanced, customized solution to address these challenges while ensuring secure and accurate digitization.
Challenges.
The task of creating an Intelligent OCR solution was formidable due to several critical requirements:
- Comprehensive Data Recognition: The system needed to digitize all data from documents, including machine-printed, uneven fonts, and handwritten text. Any unreadable text had to be flagged and resolved due to the sensitive nature of the data.
- Handwriting Recognition: The solution needed to process handwritten documents, including cursive text, within strict error margins.
- Resolution Handling: The solution was required to resolve low-quality scans automatically using AI and escalate unresolved cases for human intervention to improve future recognition through machine learning.
- High Accuracy and Efficiency: The system had to ensure near-perfect accuracy without sacrificing processing speed to manage high volumes of applications daily.
Approach.
The client collaborated with Pegasus One’s AI development team in California to design a state-of-the-art OCR solution enhanced by artificial intelligence and machine learning. The solution included the following key innovations:
- Advanced Glyph-Based Document Processing Engine: A non-legacy glyph-based engine was utilized to enable higher accuracy in reading diverse document types.
- AI-Powered Information Extraction: Custom AI models were developed to handle low-resolution document scans, ensuring seamless data capture under varied conditions.
- Handwriting Recognition with Self-Learning: A machine learning model capable of reading and adapting to various handwriting styles, including cursive, was implemented. The model improved over time by learning from cases where human input was required.
- Error Escalation and Resolution: Unresolvable cases were escalated to a human expert, and the AI was trained on the feedback to enhance future accuracy.
- Intuitive User Interface: The system was designed with a user-friendly interface to simplify interaction and facilitate easy data management.
Results.
The AI-enhanced OCR solution delivered exceptional results, including:
- High Precision: Achieved near-perfect accuracy, with errors significantly reduced compared to traditional OCR systems.
- Enhanced Efficiency: The self-learning model continually improved its performance, minimizing human intervention over time.
- Cost Reduction: The automation process reduced operational costs by eliminating inefficiencies associated with manual digitization.
- Real-Time Adaptability: The system’s ability to handle diverse document types in real time provided unmatched operational flexibility.
