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How do I decide when to use Amazon Textract vs Amazon Rekognition's TextDetect method?
My usecase is click picture from mobile and convert image data into text and store into AWS RDS.
https://aws.amazon.com/blogs/aws/amazon-rekognition-image-detection-and-recognition-powered-by-deep-learning/
https://aws.amazon.com/textract/
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Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents. It goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables.
Face-based user verification – Amazon Rekognition enables your applications to confirm user identities by comparing their live image with a reference image. Amazon Rekognition detects Personal Protective Equipment (PPE) such as face covers, head covers, and hand covers on persons in images.
OCR Text Accuracy Results when the “trouble-maker” is excluded. 90% confidence interval is shown When the “trouble-maker” is excluded, AWS Textract becomes the top performer by an almost perfect (99.3%) text accuracy level with a narrow confidence interval.
Textract can extract data in English, Spanish, German, French, Portuguese, and Italian, but it will not tell you which language was detected. Up to 10 synchronous transactions per second for the us-east-1 and us-west-2 regions; up to 1 synchronous transaction per second for other regions.
With respect to end-to-end problem solving, Textract will perform better because it is more fully featured for OCR. If you're simply trying to pull a line or two of text from a picture shot in the wild, like street signs or billboards, (ie: not a document or form) I'd recommend Amazon Rekognition.
Amazon Textract is a newer AWS service that was created as a purpose-built solution to the problem of OCR (optical character recognition) in images of documents and PDFs. While Rekognition is a more generalizable computer vision service, Textract has many more OCR-oriented tuning parameters to optimize the process of accurately and effectively extracting text.
Out of the box, if all you are trying to do is detect text and the relevant metadata (coordinates, angle, confidence value), the Rekognition DetectText method will likely perform similarly to the equivalent analyze_document method in Textract, however Textract offers further semantic structuring that helps with text curation/formatting that abstracts other forms of post-processing that the developer would traditionally need to write themselves.
Lastly, when comparing the costs of the two Detect Text methods, Textract costs a bit more ($1.50/1k images) compared to Rekognition ($1.00/1k images).
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If there is simply random text in the picture, then use Amazon Rekognition. It will find text in any location.
Amazon Textract is designed for converting paper documents into organized data. It will probably not work well with a random picture (although I haven't tried it so I can't be certain!).
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