Intelligently Extract Text & Data from Document with OCR NER

Create a virtual environment in your project folder with python -m venv and name it, for example, docu scat. Activate it via the folder's activation script to confirm successful setup.

Learn practical text cleaning techniques by removing unwanted whitespace and punctuation, normalizing to lowercase, and applying a clean text function to prepare data for analysis.

train a spaCy ner pipeline model using a config file, specify the output directory and the data and test paths, and monitor the training process until completion.

Extract text from a business card image using Tesseract, preprocess it, and feed the text to a named entity recognition model to predict names, phone numbers, emails, and organizations.

Execute the clean text function to finish data preprocessing, normalizing whitespace and punctuation, maintaining a single function, and returning clean text for use in predictions.

Get named entities from an Indian model and visualize results with spaCy displaCy, extracting phone numbers, emails, organizations such as Life Insurance Corporation of India, a person's name, and website.

Tag each word with bio tagging to identify beginning, inside, and outside entities, convert tokens and their span into a dataframe, and extract word-level text using start and end positions.

Learn to extract and visualize text and entity labels by filtering bounding box data, converting coordinates, and drawing green boxes with labels on images using ocr ner techniques.

Train version 2 model for OCR NER by preprocessing data, validating train/test paths, and running training with a config file to produce trained outputs.

Learn how to build a document scanning app using Python and OpenCV, convert documents to a text-friendly form, and apply OCR and spaCy named entity recognition to extract entities.

Resize the input image to a width of 500 while preserving aspect ratio, then apply seven-step image processing pipeline, grayscale conversion, blur, edge detection, morphological transforms, and contour-based blob detection.

Enhance and sharpen the image, convert it to grayscale, apply blur to reduce noise, and use the Canny edge detector to reveal document edges for ocr data extraction.

Create a flask-based app that uses document scanning and OCR-NER to extract text and data from uploaded images, cropping with bounding boxes and displaying fields like name and phone number.

Install the Python package Flask, activate the virtual environment, and run pip install Flask to set up your Flask app.

Design a document scanning web app with a navigation bar, upload flow, and Python-based document detection. Enable coordinate adjustment and text extraction through a named entity recognition model.

Create an about page by extending the base template with Flask and rendering about.html, showcasing the document scanning app, its computer vision and natural language processing, and data preparation steps.

Preview step 3 of predicting document coordinates in a Flask app, detailing eight substeps: static directory setup, image upload and scan, extracting four points, and returning results via Ajax.

Learn to scan documents with a Python document scanner, resize images, apply brightness and contrast, predict four-point coordinates, and save processed images for OCR workflows.

Explore the limitations of pytesseract, including issues with rotated, skewed, blurred, or handwritten text; ensure minimum image quality (200 dpi, 300x300 px) and discuss preprocessing and alternatives.