#Text extractor api code#

The Vision API would allow you to position the overlay precisely without much effort. Imagine you are an apparel company and you want users to upload an image, then try on different hats or glasses. The response includes the exact position of every facial feature (eyebrows, nose, etc), which makes it especially useful for positioning an overlay image. Facial Detectionįacial detection, not to be confused with facial recognition, is used to extract data from the people(faces) present in an image. Inspiration - Build an image-to-text tool as a PWA. It also responds with a bounding box for each object, allowing you to determine where exactly this text appears in the image. In most cases, this is all you need, but you can also get the position of each word. The first element in the response array contains the fully parsed text. The response is an array of objects, each containing a piece of extracted text. Optical Character Recognition, or OCR, is optimized by Google’s deep learning algorithms and made available in the API. Text extraction demo using Google Cloud Vision Image Text Extraction My goal is to give you some inspiration that you can use when building your next app. We’re going to explore a few of the more advanced features in Google Cloud Vision and talk about how you might use them to build an engaging UX.

#Text extractor api code#

If you’re new to functions, you can generate your backend code with the following command: firebase init functions I recommend creating a dedicated bucket for the invocation of cloud functions. You actually don’t need a frontend app to experiment with this technology - just a Firebase storage bucket. The Cloud Vision Node.js documentation is a good reference to keep by your side. This is a highly sought-after feature in business applications that still work with non-digitized text documents.

The following lesson uses the Cloud Vision API on Google Cloud to extract text from raw images.