Somewhat more disappointing was the limited range of confidence scores Rekognition returned (for each text detection, it provides a confidence score). The overall output was pretty accurate but not accurate enough for me to consider it “wow” worthy. Despite this, all of the confidence scores were above 93%.
To be considered an OCR service, AWS Rekognition has a long way to go before it’s competitive as an OCR service. It’s performance in object detection/facial recognition (which is the heart and primary usecase of Rekognition) may be better but I haven’t tested that at this point.
While visiting my family recently, I saw my dad entering numbers from each of the 5-8 ticket receipts he receives daily to keep track of the work he’s done, report for payroll, etc. I knew there had to be an easier way to collect this information without having to key each ticket manually or without using a clunky, slow scanner. After a bit of research, I found an API for OCR from Haven OnDemand and I wrote a simple script to use the camera on his laptop to snap pictures of the tickets, scrape the text and position of the text from the tickets, store it all in a MySQL database, and retain the image of the tickets in a digital archive. Demo: Snapping image via webcam and storing text
While the script works well, the API isn’t great at picking up small text or parsing large amounts of text. It lacks the accuracy (only about 80% accurate) needed to confidently rely on its interpretation of the text. To compound the issues with the API, the images from the webcam are low quality – shaky hands, varying lighting, etc. drop the quality of the images I’m trying to scrape text from.
This will be a project I keep playing with to improve the accuracy and speed of snapshots and storage. Until I can figure out a way to improve the accuracy, though, this isn’t very practical.