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FAQ

How do I convert scanned documents to editable word documents?
Well converting scanned documents to editable word documents is not that easy. Furthermore it depends whether your document is PDF or s. For the PDF's I nevere to try OCR's as I had integrated it with my Python code where I was using PyPDF2 u232 which is a python library built as a PDF toolkit. It is capable of Extracting document information (title author ...) Merging documents page by page Merging multiple pages into a single page Also there are more ways to parse PDF's using Python. So keep exploring! With the s) Google Cloud's VisionAPI (it offers powerful pre-trained machine learning models through REST and RPC APIs assigns labels to catalogue) and tesseract (Tesseract is an OCR engine for various operating systems. It is free software released under the Apache License Version 2. and development has been sponsored by Google since 26). Mathpix OCR was okay it has Python integration. For slight details it works fine. Google Cloud's Vision API worked best but since I did not find a better way to integrate it with Python and also it was billable so I left it. For Tesseract in the windows I had to install Tesseract then I integrated it with my Python code using the pytesseract library of Python and it worked great. At least it did not ask for billing unlike Google Cloud's Vision API. So now you have the s of s to editable word documents where you just have to upload your scanned documents on their sites and they will convert them to word documents and then you can download them. However they are not 1% accurate. Also there are privacy issues when using the online OCR's. If you find it useful like it. Also please help me inments if youe across better OCR's or ways to convert scanned documents to editable word documents and if you get a way to use Google Cloud's VisionAPI without enabling billable.
How can I implement machine learning algorithms in a web application?
From your question I inferred you are talking about online applications. Obviously there are other applications like standalone medical devices etc. that have a different story. Assuming that let's divide the problem into fourponents 1- You need a database. Your choice depends on different aspects but most important thing is size and speed of your data. For small sized problems a regular RDBMS will do the job. 2- You need aponent to build dynamic HTML pages. A typical web programming language like PHP will do that job. Your dynamics HTMLponent managesmunication with the database on front end. 3- You need a beautiful and easy to use front-end. The skills required are CSS Javascript and HTML. Thisponentmunicates to (and partially is generated by)ponent 2. 4- Finalponent is your ML engine. You can write it in any language but performance and of application are the most considerations. For large distributed applications your choicees down to Hadoop or Spark ecosystems. For mid-size data sets you can use Java and C++. If you have a small size data R and MATLAB can be used. Your ML-engine mightmunicate with the database directly (usually if it's a large application or involves online learning) or might not (if you have another mechanism to periodically extract data and update your ML-engine). The results of the ML-engine is the feed for your 2ndponent engine. Something among a typical relational database a file or JSON file ismon here. As you can see different skills are involved for a production web-based ML application. In enterprise level applications firstponent is performed by a data engineer second one by a software (web) developer third one by a graphic designer (UI engineer) and last one is the work of a data scientist. Please follow me if you like to hear more about data science and artificial intelligence.