Understanding alternat

alternat features are centered around tasks. Following table features break up across each task:

Task	Description	Options	Details
Collection	Scans the website and downloads images	Uses puppeteer to crawl the web page.	We are using apify - A puppeteer scrapper that crawls website using the headless chrome https://apify.com
Generate	Generates alt-text using, image captioning, OCR and images labels	Azure ML API	Use azure CV API for caption & OCR
		Google ML API based.	Use Google vision OCR and Image Labelling
		Open source based.	Use pytorch based model for OCR (EasyOCR) as well as image captioning

Library offers the flexibility of choosing either or both tasks and selecting suitable options from each task. Options are called drivers in alternat lingo. So, if you want to use azure for alt-text generation then you initialize the generator with azure driver. Same goes for google and “opensource” driver. Read the options as drivers.

There are few reasons for providing 3 drivers:

• Azure and google gives ready to use API, essentially lowering the barrier to get started.

• Most of the organizations don’t have the data to train their own model for OCR and image captioning.

• Open source is a free alternative but can be little less accurate in few situations.

The tradeoff here is between cost and accuracy.

The OCR function is responsible for reading text from images. However, most of the ML API for OCR would treat single line as one text blob and might lead to unexpected out-of-order OCR text. For this reason, alternat comes with its own clustering implementation for OCR. alternat by default applies a clustering algorithm to create nearby data as a single text blob and combines them into a single line thereby generating more in-order human friendly OCR text.