Data Extraction Basics for Docs and Images with OCR and NER

Master step-by-step tools setup for data extraction, data labeling, training a model, and model prediction, covering Windows and Ubuntu environments with reference setup manuals.

Learn how to set up and use PyCharm for Python development, including creating or opening projects, configuring a Python interpreter, managing virtual environments, installing packages, and running or debugging code.

Outline the ocr environment basics, cover pixels and image properties, explain kernels for dimensionality reduction without losing context, introduce feature maps, and guide hands-on Jupyter notebooks and code downloads.

Explore how computers represent images: pixels as the smallest units in a two-dimensional grid, grayscale 8-bit values and rgb color with 0–255 intensities forming black, white, and colors.

Explore image properties, from pixels and color channels to shape, RGB and BGR formats, grayscale conversion, and color spaces like Lab and HSV, using NumPy, Pillow, and OpenCV.

Explore how a kernel—a 3x3x3 filter with weights—slides over a 32x32 color image, creating receptive fields and enabling blurring, sharpening, and embossing.

Convolve the input by sliding a kernel and multiplying pixel values to form a feature map via dot products; 32x32 input with a 3x3 kernel and stride 1 yields 30x30.

Unlock text from images using Tesseract, an open source OCR engine, with training data, language options, and a PyTesseract wrapper for line OCR and adjustable page segmentation and engine modes.

Explore Tesseract's 14 page segmentation modes and OEM options to tailor OCR, using PSM for single text blocks and choosing legacy, LSTM, or both for accurate region extraction.

Explore pytesseract, a wrapper for the tesseract OCR engine, to extract text and bounding boxes from JPEG, PNG, GIF, BMP, TIFF images with language options and oem/psm settings.

Implement tesseract ocr using pytesseract in python with OpenCV, demonstrating three scenarios: full image, cropped characters, and cropped words, with bounding boxes and data extraction in a Jupyter Notebook.

Develop an understanding of data conversion and extraction across formats, starting with structured pdf and scanned pdf, then word documents and common pipeline formats, and finish with code download instructions.

Convert all document types to text by extracting pdf and word data with Pypdf4, handle embedded images using OCR, and standardize to a single format for machine learning pipelines.

Convert and extract structured data from a pdf invoice using PyPDF4 in Python, configure Python 3.6, set the input path, extract text, and save to pdf_to_text.txt.

Convert a scanned pdf document into an image using pdf2image's convert_from_path, configure poppler on windows, set the python interpreter, update environment paths, and use the input file Invoice Bill Scanned.jpg.

Learn how to standardize inputs by converting all document types — structured PDF, scanned PDF, and Word — to a common image format, enabling OCR-ready text extraction.

Learn to extract data from images using OCR with PIL, OpenCV, and Tesseract basics, including page segmentation mode and OEM, plus PyTesseract operations and a code walkthrough with download instructions.

Learn to read images with PIL and OpenCV in Python, convert them to arrays with numpy, and display and save the images while noting rgb and bgr channels.

Explore tesseract's page segmentation mode (psm) and OCR engine mode (oem) to extract text from images, using 14 psm options and legacy or lstm engines.

Explores how pytesseract, a wrapper for tesseract-ocr, converts images to text, boxes, data, osd, or pdf/hocr, with configurable oem and psm settings.

Follow a code walkthrough to extract text from an image using pytesseract and cv2, converting to grayscale and saving results to extract_text.txt, with emphasis on psm 12 and language English.

Download Python file and input data from Resources section, create a PyCharm project, open Python file, and run the code for image reading with PIL and OpenCV and data extraction.

Explore named entity recognition with spaCy, learn entity types and the IOB format, label data, train a custom NER model, and run predictions from the supplied code.

Explore named entity recognition (NER) within natural language processing, locating and classifying entities such as person and location names using rule-based, unsupervised, and supervised methods, with deep learning now dominant.

Discover Spacy, a free open-source NLP library for data extraction and preprocessing, featuring tokenization, POS tagging, dependency parsing, lemmatization, sentence boundary detection, and named entity recognition.

Explore spaCy's named entity recognition (NER) with its default model, identifying entity types such as Person, FAC, ORG, GPE, Date, Money, and cardinal in unstructured text.

Explore the IOB format, a tagging system for chunking in named-entity recognition, where B marks the beginning, I marks inside, O marks outside, and no entity tag is indicated.

Label entities with Spacy for ner, view iob formatted outputs, and explore tagging orgs and person names while preparing for custom data training.

Use transfer learning to train a spaCy NER model on custom data, tagging 'Diplomatic' as a FAC entity, and serialize with DocBin before training and saving the updated model.

Download all Python, data, and configuration files from Resources, create a PyCharm project, and run the included Spacy NER scripts for labeling, training, and predicting.

Explore how to convert data to csv using pandas, starting with a high-level pandas overview, followed by a code walkthrough and execution steps.

Explore pandas for tabular data, learning about series and dataframes, their labeled structure, and how read_csv imports csv data into a dataframe for analysis.

Load the spaCy model, process OCR output to extract entities, and assemble them into a dataframe before exporting to CSV with pandas to_csv.

Download the Python and input data files from the Resources section, create a new PyCharm project, and run the code using the included configuration to produce CSV output.

Build and understand the Smart Data Extractor pipeline to extract data from structured and scanned PDFs and Word documents, with a detailed code walkthrough and download guidance.

Walk through a data processing pipeline that converts pdfs and Word documents to a common image format, applies ocr with Tesseract, labels with spaCy, and exports a final csv report.

Showcases the smart data extractor project that processes pdf, image, and docx files using OCR and NER, producing CSV outputs and text copies of extracted data.

Download Smart_Data_Extractor.zip from Resources section, unzip it, and run main.py in PyCharm to execute the Smart Data Extractor, which includes Python files and data in PDF, Word, and image formats.

Learn how to extract data from documents and images using OCR concepts, and access direct links to Python OCR, OpenCV, and Pandas courses to deepen your skills.