Million pages processed
Accuracy
Years of manual effort saved
The client required a large number of engineering documents to be stamped and authenticated by a dedicated team of engineers for verification and audit purposes. However, identifying the stamped documents, which range from size and number of pages, was a manual, unstructured, and time-consuming process.
Furthermore, the stamps are not consistently placed, sized, clearly visible, or have the same signatures of the engineers. Therefore, the client wanted an automated solution that could reduce the manual efforts involved in searching for stamps and classifying documents.
Amazon S3
Amazon SQS
Amazon SageMaker
Amazon DynamoDB
AWS SFTP
Amazon Elasticsearch
Quantiphi leveraged machine learning to develop a solution that helps accurately detect the presence of stamps in engineering documents and store them along with the file name for easy access, audit, and verification purposes.
