Model accuracy for printed text
Model accuracy for handwritten text
Processing time per check
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.
Google Cloud Platform
TensorFlow
Python
Compute Engine
Cloud Storage
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.
