case study

Automated Check Image Processing

Banking & Financial Services

Business Impacts


Model accuracy for printed text


Model accuracy for handwritten text


Processing time per check

Customer Key Facts

  • Location : North America
  • Industry : Financial Services

Problem Context

Financial Services institutions need to process thousands of checks on a daily basis. Manually going through the check images to extract information is a very time-consuming process. While the structure of endorsements is similar across financial institutions, the vast variation in templates and types of handwriting constitutes a true challenge.



  • Huge variation in the format and layout of checks
  • No access to real data that resulted in mock data generation
  • Manual effort needed for the check tagging and endorsement verification
  • Dealing with a third-party software for the information extraction process resulting in overhead costs

Technologies Used

Google Cloud Platform

Google Cloud Platform





Compute Engine

Compute Engine

Cloud Storage

Cloud Storage

Automating Manual Bank Check Screening Process Using Deep Learning


Quantiphi developed a custom machine learning-based solution to automate the detection of fields of interest and extraction of the corresponding information. The solution processes and captures check details furnished by customers in less than two seconds with no manual intervention and with a detection accuracy of over 96 percent across all fields.

Minimal manual interruption enables a near error-free check endorsement process, with reduced misinterpretation of handwriting and input typos/incorrect spellings recorded. The solution is scalable and PII compliant; thus reducing costs and security issues at production-level.


  • Real-time solution: A single check image will process and display information in around three seconds
  • Reduced manual effort

Thank you for reaching out to us!

Our experts will be in touch with you shortly.

In the meantime, explore our insightful blogs and case studies.

Something went wrong!

Please try it again.