#Crawling #Scrapping #DataExtraction #Crawlers #AI #DeepLearning #AIML

Crawling usually refers to dealing with large data-sets where you develop your own crawlers (or bots) which crawl to the deepest of the web pages. Data scraping, on the other hand, refers to retrieving information from any source (not necessarily the web).

Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis.

Web scraping a web page involves fetching it and extracting from it. Fetching is the downloading of a page (which a browser does when you view the page). Therefore, web crawling is the main component of web scraping, to fetch pages for later processing. Once fetched, then extraction can take place. The content of a page may be parsed, searched, reformatted, its data copied into a spreadsheet, and so on. Web scrapers typically take something out of a page, to make use of it for another purpose somewhere else. An example would be to find and copy names and phone numbers, or companies and their URLs, to a list (contact scraping).

Web scraping is used for contact scraping, and as a component of applications used for web indexing, web mining and data mining, online price change monitoring and price comparison, product review scraping (to watch the competition), gathering real estate listings, weather data monitoring, website change detection, research, tracking online presence and reputation, web mashup and, web data integration.

Web pages are built using text-based mark-up languages (HTML and XHTML), and frequently contain a wealth of useful data in text form. However, most web pages are designed for human end-users and not for ease of automated use. Because of this, tool kits that scrape web content were created. A web scraper is an Application Programming Interface (API) to extract data from a web site.

There are methods that some websites use to prevent web scraping, such as detecting and disallowing bots from crawling (viewing) their pages. In response, there are web scraping systems that rely on using techniques in DOM parsing, computer vision and natural language processing to simulate human browsing to enable gathering web page content for offline parsing.

FcosAI scrapped thousands of articles from several newspaper websites like Times of India, NDTV News, India Today, Business Standard, etc. The same has been done for popular e-commerce websites like Flipkart.

FcosAI's dedicated in-house system can crawl millions of data points across publicly available sites. We have a crawler for both images and text. We provide services, starting from data crawling to data labeling for our client's AI/ML model as per their requirements and the results are provided in their desired format (JSON, CSV, etc).

The tools used are highly efficient to deliver the services at a minimal cost.

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Updated: Jan 15, 2020

#ImageSegmentation #ImageProcessing #AI #DeepLearning #AIML

In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as image objects). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

The result of image segmentation is a set of segments that collectively cover the entire image or a set of contours extracted from the image. Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s). When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of interpolation algorithms like Marching cubes.

At FcosAI, we build an image segmentation tools which is completely automated and could be used to segment any type of image to identify the most visually prominent object in it.

We use a recent deep learning-based approach for the segmentation.

We provide API to integrate with your system for bulk segmentation. Below are a few example images that are segmented using our tools automatically.

From the above examples, you can see we build a class agnostic object segmentation model that generalizes well for any type of object. There are a lot of use cases of these tools. Any Individuals and Photographers could use this for removing background without any editing.

This segmentation tool could be used for creating your Marketing content.

For e-commerce, this is a great tool to create clean product taxonomy by removing the noisy background. These tools could also be used for generating segmentation data for your AI model.

If you are looking to scale up your image segmentation process, try FcosAI.

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Accurate bounding box labeling is most important for the best performance of an object detection model. At FcosAI, our dedicated highly trained annotator performs annotation for various tasks starting from bounding box labeling for e-commerce product type detection, pedestrians detection, blood cell detection, car's number plate detection and many more. We provide the annotation output as the customer specified any format. This annotation box could be used for object detection, object classification, weekly supervision for semantic segmentation to empower your AI model. Following are few use cases we've handled -

1. E-Commerce Product Types Detection

This type of bounding boxes could be used for e-commerce product type detection which is the main component for product search engines based on visual/image similarity, product matching, and recommendation system. We provide detailed labeling starting from small fashion accessories to large objects like furniture and all.

2. Pedestrians Detection

Pedestrian detection dataset can be used for various security anomaly identification problem in traffic signal, road, footpath. Our bounding box labeling for pedestrians detection can enrich your model for this purpose.

3. Blood Cell Detection

This type of annotation could be used for a medical diagnosis for different blood cell detection. The diagnosis of blood-based diseases often involves identifying and characterizing patient blood samples. Automated methods to detect and classify blood cell subtypes have important medical applications.

4. Vehicle Number Plate Detection

Vehicle Number Plate Detection aims at the detection of the License Plate present on a vehicle and then extracting the contents of that License Plate. A vehicle’s license plate is commonly known as ‘a number plate’. It is a metal plate that is attached to a vehicle and has the official registration number of a vehicle embossed on it. We provide bounding box and text extraction annotation for vehicle registration plat detection.

If you are looking to scale up your image labeling needs to empower your model, try FcosAI.

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