I will present some useful Python code that can be easily used in other similar cases (just copy, paste, run) and walk through every line of code with comments, so that you can easily replicate this example (link to the full code below).
Github blockblock how to#
In this tutorial, I’m going to show how to use OCR for Document Parsing. Basically a sort of Object Detection for text. Today, the field has reached a very high level of complexity mixing image processing, text localization, character segmentation, and character recognition. It was invented during World War I, when Israeli scientist Emanuel Goldberg created a machine that could read characters and convert them into telegraph code. OCR (Optical Character Recognition) is the process of detecting and extracting text in images through Computer Vision.
It can be tricky as in several cases there is no clear answer to what the expected result should look like (text, images, charts, numbers, tables, formulas, …). Document Image Analysis refers to techniques applied to images of documents to obtain information from pixel data. Just imagine having to go through 100 pages manually searching for a table, just to copy and paste it somewhere else… how cool would it be having a program that does it in 1 second?Ī popular strategy for parsing is to convert the document into an image and employ Computer Vision. It is essential for companies as it reduces a lot of manual work.
Document Parsing involves examining the data in a document and extracting useful information.
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