How we were able to auto-scale an Optical Character Recognition Pipeline to convert thousands of PDF documents into Text per day using event driven microservices architecture driven by Docker and Kubernetes
On a recent project we were called in to create a pipeline that has the ability to convert PDF documents to text. The incoming PDF documents were typically 100 pages and could contain both typewritten and handwritten text. These PDF documents were uploaded by users to an SFTP. Normally, on average there would be 30–40 documents per hour, but as high as 100 during peak periods. Since their business was growing the client expressed a need to OCR up to a thousand documents per day. These documents were then fed into an *NLP *pipeline for further analysis.
Time to convert a 100-page document — 10 minutes
Python process performing the OCR consumed around 6GB RAM and 4 CPU.
We needed to come up with a pipeline that not only keeps us with the regular demands but can auto-scale during peak periods.
We decided to architect a serverless pipeline using event driven microservices. The entire process was broken down as follows:
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