Introduction
The post-coronavirus pandemic world depends profoundly on mobile apps for its shopping. Research indicates that average smartphone users spend more than 3 hours on phones daily, and one of every five users spends more than 4.5 hours every day looking at the phones! Thinking about the daily average time on mobile phones, it's no wonder that eCommerce apps are quickly replacing eCommerce sites and becoming the leading shopping channel. To stay ahead in the competition, Actowiz Solutions has introduced a pioneering mobile app scraping solution to accompany its market-tested eCommerce web scraping technology. It’s time to get a closer look at this technology and the procedure behind mobile app scraping:
With two ways you can scrape mobile apps data:
Scenario 1
While composite APIs get open (e.g., Amazon) - In those cases, a smaller setup is associated, but ultimately the extraction does not vary much to the standard sites.
Scenario 2While composite APIs get encrypted (e.g., Dollar General, HEB, Target, Stop & Shop, etc.). The case has become much more complex and needs specialized mobile app extraction, OCR, and other machine learning methods to get deployed. Here is a brief explanation of Actowiz Solutions’ approach:
Methodology
Phase 1) Recording a mobile navigation/session (using special visual navigation extractors)Technology consideration: To make the procedure scalable, we use real device clouds and match many devices that use our more intelligent proxy networks.
Phase 2) Recognizing product ROI (Region of Interest)
As there are 1+ products in the single frame, extracting text for any particular product is very difficult. If tried, it may lead to mismatched data. To deal with this, we have used Object Detection algorithms as our initial step to getting the ROI of every product, despite how many products are in the single frame.
Input files get passed to YoloV5, a custom fine-tuned for identifying the ROI of the products. The architecture YoloV5 has a benefit over various models due to its quick and precise inference.
The ROI taken from the video is cached in the form of images. The video has certain number of frames within a second that can result in product duplication. To deal with this, we have introduced a deduplication stage in which we could remove some product ROIs which are matching. This further assists in processing data more quickly and effectively.
Phase 3) Recognizing Product Components
Components like pricing, product image, information, etc., are recognized in the given stage (Components might differ depending on an app). YoloV5 is used (different example vs. step 2 above as we require precise level attention to recognize the components)
Phase 4) Scraping Components with OCR
In the given stage, we can finish textual extraction as all the details of products have been recognized. A customized OCR framework is used to get a detailed text extraction.
Phase 5) Retrieving Final Outputs
The text scraping output and metadata are stored in a database. Here, the data transform to an anticipated client format and becomes ready to get sent/ requested/uploaded using API. Unit tests with quality checks get applied.
Conclusion
Just because the retail economy is moving further away from old-style brick and mortar to a contemporary, technologically advanced option like As and mobile social commerce and an up-and-coming metaverse, retail requires to get prepared with pioneering AI and analytics to hinge quickly. Actowiz Solutions knows this and provides retailers and brands with the most sophisticated and accurate analytics across the retail landscape. It advances its goals by making solutions like an advanced mobile app extraction. Actowiz Solutions protracts its exclusive technology using a metaverse to extract retail stores, including key platforms like Meta, Roblox, Sandbox, Decentraland, etc.
For more information, contact Actowiz Solutions now! You can also reach us for all your mobile app scraping service and web scraping service requirements.
