SBL
Case Study:

Revolutionizing Genealogical Research with AI-Driven Handwriting Recognition

 

In the realm of historical and genealogical research, the ability to access and interpret handwritten documents is of paramount importance. For one renowned genealogy publishing house, the process of manually transcribing these documents had become a significant bottleneck. As the volume of historical documents grew exponentially and the complexity of handwritten texts remained a challenge, the publisher sought a solution to streamline their transcription process.

Recognizing the potential of artificial intelligence to transform this labour-intensive task, the genealogy publishing house partnered with SBL Technologies. The goal was clear: develop an innovative AI-driven handwriting recognition system that could accurately and efficiently transcribe historical documents, unlocking valuable insights for researchers worldwide.

Customer

 

Who we worked with:

  • A renowned genealogy publishing house, a leader in historical and genealogical research

 

What the customer needed:

  • To streamline the labor-intensive process of manually transcribing historical documents
  • To develop a system capable of handling diverse handwriting styles found in centuries-old documents
  • To systematically index vast amounts of data for easy retrieval and accessibility
  • To create a scalable solution that could keep pace with the growing archive and increasing demand for rapid data access and accuracy

 

How we helped:

  • Developed a custom AI-driven OCR tool specifically tuned to recognize a variety of handwriting styles from different eras and regions
  • Engaged high-level genealogists to annotate thousands of documents, training the AI models to identify crucial genealogical data
  • Incorporated a continuous learning feedback loop, allowing the system to adapt and improve accuracy over time
  • Designed a scalable data processing framework for efficient batch processing and indexing of documents

Challenge

 

The primary challenge was the inefficiency and error-prone nature of manual transcription methods.

The client needed a system capable of handling diverse handwriting styles found in documents dating back centuries, as well as the ability to systematically index vast amounts of data for easy retrieval. The traditional method was not scalable, with the growing archive and the increased demand for rapid data access and accuracy.

 

 

Approach

 

SBL Technologies’ solution involved several advanced technologies and methodologies:

  • AI-Driven Handwriting Recognition:
    • Development of a Custom OCR Tool: Leveraging deep learning algorithms, a custom Optical Character Recognition (OCR) tool was developed specifically tuned to recognize a variety of handwriting styles from different eras and regions.
    • Data Annotation and Training: High-level genealogists annotated thousands of documents to train the AI models. This training included identifying and marking crucial genealogical data such as names, dates, locations, and relationships.

 

  • Integration of a Learning Feedback Loop:
    • Continuous Improvement Mechanism: The system incorporated a feedback loop where outputs were systematically checked by genealogists, and corrections were fed back into the AI system to continuously improve accuracy and adapt to new handwriting styles.

 

  • Scalable Data Processing Framework:
    • Batch Processing and Indexing: The system was designed to process documents in large batches, automatically indexing extracted data, thus enabling dynamic scalability and efficient handling of increasing document volumes.

 

 

Benefits

 

The AI-based transcription system delivered transformative benefits:

  • Enhanced Accuracy and Speed: Initial accuracy levels of 70% improved to over 95% with continuous learning, dramatically reducing the time from document receipt to data availability.

 

  • Scalability and Efficiency: The system’s ability to adapt and learn from new data ensured it could expand its capabilities without requiring linear increases in human resources or time.

 

  • Improved Data Accessibility: By creating highly accurate and searchable indices, the publishing house could offer quicker and more reliable access to historical data, enhancing research capabilities for users worldwide.

 

 

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