Computer Vision : OCR using Python - GenAI with LLM & RAG

Discover the objectives of optical character recognition (ocr), including its architecture, industry solutions, accuracy and pricing, benefits, and momentum across finance, legal, healthcare, and general business.

Master optical character recognition (OCR) to convert images into editable, searchable text. Apply the three-step pipeline—text detection, text recognition, and text labeling—and compare Tesseract, Abbyy, Google Cloud Vision, and Microsoft Vision.

Explore the three-step OCR architecture—text detection, recognition, and labeling—and how bounding boxes, tesseract/easyocr, and named entity recognition (ner) via spaCy enable key-value extraction in json or xml.

Compare industry OCR solutions, including Tesseract, Abbyy, Google Cloud Vision, and Microsoft Computer Vision, examining accuracy on ordinary invoices versus identity documents, key challenges, and pricing.

Discover how OCR reduces costs and boosts productivity by digitizing data, improving accuracy, and speeding document processing while enhancing data security, accessibility, and compliance.

See how OCR enables automated processing across finance, legal, healthcare, travel, and retail, extracting invoices, bank statements, contracts, birth and death certificates, and patient records for digital onboarding and auditing.

Explore the OCR concept and architecture, compare open-source and commercial OCR solutions, and follow a step-by-step tool setup for Ubuntu and Windows while addressing common setup challenges.

Resolve Windows 10 Pytesseract issues by updating tesseract_cmd to the full path on the C drive under Python36, enabling seamless OCR with tesseract.

Create, upload, and run notebooks in Google Colab. Mount Google Drive by GUI or code in Colab and select CPU, GPU, or TPU runtimes with 12-hour limits.

Learn how to set up and use PyCharm for Python coding, including creating projects, configuring Python interpreters and virtual environments, installing packages, running and debugging code with breakpoints.

Download the Jupyter notebook from the resources section, launch it from the command prompt, and run code blocks with Run or Shift+Enter, then explore shortcuts with Esc+H.

Set up the OCR environment, explore pixels and image properties with a downloadable Jupyter notebook, and learn kernels and feature maps to prepare for text detection by running code.

Understand how a digital image is formed from pixels, the smallest picture elements in a 2d grid, with black-and-white, grayscale, and color images using 0–255 rgb channel values.

Learn image basics by reading images with PIL and OpenCV, convert to arrays, and inspect shape, height, width, and color channels, including grayscale conversion and RGB, HSV, and LAB spaces.

Explore how a kernel slides over an image, multiplies with input pixels in a 3x3 receptive field, and produces enhanced outputs for blur, sharpen, and emboss effects.

Define a feature map as the result of sliding a filter over an image with a stride. A 32×32 input and 3×3 kernel produce a 30×30 feature map via N−F+1.

Master the text detection workflow, including pre-processing, noise removal, and segmentation, and implement CTPN and EAST for improved OCR accuracy in Python with Jupyter notebooks and Google Colab.

Identify and localize text in images through a text detection workflow that pre-processes images to remove noise, then segments content by characters, words, or lines for reliable detection.

Improve OCR accuracy by applying image preprocessing techniques, including binarization, thresholding, adaptive thresholding, and Otsu binarization, with Gaussian blur, resizing, and noise removal to optimize text detection and recognition.

Master noise removal for OCR preprocessing, covering morphology with kernels, small contour noise removal, image blurring, dilation, erosion, deskew, and border handling to boost accuracy.

Explore image preprocessing techniques for OCR, including binarisation, adaptive and Otsu thresholding, gaussian blur, rescaling, noise removal, morphology, deskewing, border removal, and padding using OpenCV.

Learn image text segmentation after noise removal, breaking images into line, word, and character blocks, including cursive handwriting, with code-level implementation in the next lecture.

Implement character, word, and line level segmentation in Python using imread, grayscale, blur, adaptive thresholding, dilation, erosion, and contour-based bounding boxes, as demonstrated in the segmentation notebook.

Explore why OCR is needed and the benefits of free open source tools. Compare Tesseract, calamari, and OCRopus to evaluate strengths, limitations, and multilingual support for handwritten text.

Identify why OCR matters in a data-rich world by bridging paper documents and digital systems. Extract text from images to enable document scanning, data extraction, indexing, search, and accessibility.

Explore the cost effectiveness and licensing-free advantages of free and open source OCR. Benefit from customization, transparency, and a global community to improve accuracy and add features.

Explore the robust open-source OCR engine Tesseract, tracing its architecture from image pre-processing to layout analysis, character recognition, and post-processing, with multilingual capabilities and practical applications.

Calamari is a deep learning based ocr tool for handwritten text, featuring a pre-processing, feature extraction, and sequence modeling with rnn or lstm and multilingual support.

Discover OCRopus, a modular open-source OCR tool with a pipeline from image pre-processing to post-processing, featuring layout analysis, line and word segmentation, and character recognition for document digitization.

Compare three OCR tools: Textract, calamari, and OCR opus; Textract suits printed text, calamari excels at handwritten text, and OCR opus handles complex layouts; test languages and document quality.

Explore cloud based computer vision APIs and OCR capabilities to extract insights from images and videos, and compare cloud vision services to select the right tool for your project.

Discover how cloud-based computer vision removes upfront hardware costs, scales with demand, and delivers pre-trained models for detection and classification. See impacts in healthcare, retail, manufacturing, and security.

Explore Abby Cloud OCR, a cloud-based service that converts scanned documents, PDFs, photos, and screenshots into searchable Word or Excel files, enabling data extraction and integration with document management systems.

Explore Abbey cloud OCR’s industry-leading accuracy across 200 languages and input formats, with intelligent document processing that extracts names, addresses, dates, and invoice items, and supports customizable dictionaries and security.

Explore Google Cloud Vision's text detection and document text detection OCR, landmark recognition, and image analysis to extract text, identify landmarks, and derive insights within the GCP ecosystem.

Explore Azure Computer Vision, a suite of vision APIs for object detection, facial recognition, content moderation, and OCR, with image description and visual feature analysis.

Explore Azure computer vision's advanced object detection, scene understanding, facial recognition, and content moderation to enable applications in autonomous vehicles, retail analytics, CRM, and AR.

Evaluate Abbey cloud, Google Cloud Vision, and Azure Computer Vision to define needs like OCR, object detection, and facial recognition, compare accuracy, pricing, integrations, and scalability, and choose the fit.

Explore cloud vision use cases across healthcare, retail, manufacturing, and security, highlighting automation, insights, and real-world applications like medical image analysis, product recognition, and facial recognition.

Explore the future of cloud vision through automation, data extraction, and enhanced user experiences enabled by abbey cloud, Google Cloud Vision, and Azure Computer Vision, plus edge computing and algorithms.

Explore case studies of AB cloud OCR, including insurance claims, safeguarding public places, azure computer vision, and manufacturing optimization, google cloud vision for fashion e-commerce and content moderation, with resources.

Explore retrieval augmented generation (rag) and integrate optical character recognition (ocr) to boost capabilities, covering rag architecture, indexing, retrieval and generation roles, frameworks, and a hands-on code walkthrough.

Explore the foundations of large language models, from tokens and self-attention to transformers, and architectures like GPT, BERT, T5, palm, and Gemini.

Explore four architectural patterns to customize LLM applications, from prompt engineering to training from scratch, covering lightweight techniques, retrieval augmented generation, and fine-tuning for domain adaptation.

Retrieval augmented generation solves the challenges of static knowledge cutoffs and hallucinations by separating the knowledge base from the language model, enabling real-time retrieval and customizable outputs without retraining.

rag merges information retrieval with generative models to produce up-to-date, context-rich outputs by querying a document store and external sources through a retrieval model and a language model.

Explore the RAG architecture for document QA, with offline indexing via OCR, document loaders, text splitters, a vector store, passage retrieval with MIPS and Bert, plus a Bert-large generator.

Explore indexing with vector embeddings that represent items and user interactions in high dimensional space to power personalized retail feeds using faces for similarity search.

Explore how retriever and generator work in RAC architecture, showing how retrieval builds an index like library catalog to find relevant information, and how the generator crafts a coherent response.

Explore rag model frameworks like LangChain, Llama Index, Haystack, and Hugging Face, plus Google Search Genomics, Amazon Bedrock, and GPT-4, with Pinecone and Faiss for vector storage.

RAG combines information retrieval and generative AI to ground outputs in real-world facts, expand knowledge bases, and enhance customization, cost efficiency, and user experience.

Integrate an OCR step into the RAG pipeline to extract text from images, enabling scanned documents and images with text overlays to feed the document loader and downstream generation.

Build a retrieval-augmented generation (rag) pipeline for ocr documents using embeddings and a vector store, connected to a mistral/llama model via LangChain for qa.

Explore the building blocks of deep learning, starting with neurons and their architectures, then delve into artificial neural networks and convolutional neural networks, and conclude with activation functions.

Define a neuron as the computing unit of a neural network, receiving inputs through dendrites, computing and transmitting outputs via axons, and learning like a perceptron with training data.

Explore how a neuron, modeled on the human brain, uses weighted inputs, a bias term, and an activation function to classify iris flowers within an artificial neural network.

Explore artificial neural networks (ANNs) and their supervised, layered architecture—input, hidden, and output layers—trained by fine-tuning weights and activation functions for tasks like classification, speech recognition, and computer vision.

Learn how convolutional neural networks extract features from images via convolutional layers, pooling (max, average, sum), and fully connected layers for efficient image classification with 2d convolutions on 3-channel inputs.

Activation functions drive deep learning outputs and training efficiency, acting as gates for each neuron and covering binary step, linear, and non-linear types.

Apply the CTPN model to detect text in natural scene images, using an end-to-end trainable bidirectional LSTM for robust, multi-scale, multi-language localization with text/non-text scores and side refinements.

Learn the east model for fast, accurate scene text detection with a single fully convolutional network that predicts per-pixel text scores and rbox or quad geometries.

Explore easy OCR concepts and code-level implementation, then learn tesseract basics, PSM and OEM modes, CPTPP and east text recognition techniques with Google Colab via detailed code walkthroughs.

Discover EasyOCR, a Python PyTorch OCR tool supporting 80 languages and reading natural scene text and dense text, with craft-based detection and CRNN recognition, set up via pip install.

Demonstrates installing easyOCR with pip, loading the English package, and using read text to extract and display detected text and bounding boxes in images.

Tesseract is an open source OCR engine under Apache license that supports languages and can extract text from images via command line or PyTesseract, with PSM and OEM options.

Explore Tesseract page segmentation modes and OEM options, learn how PSM 6 targets a single uniform text block, compare legacy and LSTM engines, and review available OEM values.

Learn how PyTesseract wraps the Tesseract-OCR engine to read image types supported by Pillow and Leptonica, extract text with image_to_string, and produce bounding boxes, data, and pdf or hocr outputs.

Learn to implement tesseract OCR in Python using pytesseract and OpenCV, with a Jupyter notebook, covering full-image text, character cropping, and word cropping with bounding boxes.