Course Syllabus

  Module 1: Introduction to OCR and NLP – This module introduces Optical Character Recognition (OCR) and Natural Language Processing (NLP), explaining how scanned documents are converted into editable text and then analyzed. Learners will understand OCR pipelines (image → text) and fundamental NLP tasks. They will see examples of real-world applications (e.g. extracting text from invoices or forms). By the end, students can distinguish OCR vs. NLP roles in document workflows.

  Module 2: Python Environment and Key Libraries – Learners set up the Python environment and explore essential libraries: PyTesseract (Python wrapper for Google’s Tesseract OCR) and OpenCV for image processing; NLP libraries like NLTK, spaCy and Hugging Face Transformers for text analysis; and tools for deployment (Flask, Docker). This module covers installation, configuration, and basic usage, enabling students to run OCR and NLP code.

  Module 3: Image Preprocessing for OCR – Focusing on OpenCV techniques, this module teaches how to clean and enhance scanned images (grayscale, thresholding, noise reduction, deskewing) before OCR. We discuss how OpenCV’s computer-vision tools improve OCR accuracy. Hands-on exercises will include detecting text regions and preparing images so that Tesseract can recognize characters reliably.

  Module 4: Performing OCR with Tesseract – This module covers practical use of Tesseract via the PyTesseract library. Students will learn to extract printed text from images and PDFs, and handle different languages/fonts. For example, they will use PyTesseract to convert invoice or form images into raw text. Key objectives include understanding OCR output formats and improving OCR results through parameter tuning.

  Module 5: Text Post-Processing and Cleanup – After OCR, raw text often contains errors or artifacts. This module trains students in cleaning techniques: removing noise, correcting common OCR mistakes, and formatting text. It emphasizes NLP preprocessing steps like tokenization, stop-word removal, and stemming. Learners will apply these to prepare OCR-derived text for analysis; as data extraction experts note, data cleaning and tokenization are essential for accurate results.

  Module 6: Introduction to NLP with NLTK – Students explore basic NLP using the NLTK library. Topics include text tokenization, part-of-speech tagging, and simple pattern matching. Practical exercises use NLTK to parse sentences from OCR text, perform frequency analysis, and implement simple keyword extraction. This module builds foundational NLP skills for handling textual data from scanned documents.

  Module 7: Advanced NLP with spaCy – This module delves into industrial-strength NLP with spaCy. Learners will use spaCy models for named entity recognition (NER) and dependency parsing on cleaned text. For example, they will extract entities like names, dates, and amounts from an invoice or contract. Students will also learn about SciSpacy for specialized medical/biomedical entity extraction, as used in healthcare document analysis.

  Module 8: Transformer Models and Hugging Face – Focusing on state-of-the-art NLP, this module introduces transformer-based models (e.g. BERT, RoBERTa) via the Hugging Face library. Students will fine-tune pre-trained models for tasks like text classification, summarization, and QA. We discuss how transformers can be applied to document data (e.g. summarizing a long legal text or classifying document type). Learners will practice with Hugging Face pipelines for rapid prototyping of NLP tasks.

  Module 9: Document Digitization and Summarization Project – In a project-based module, students build an end-to-end system that digitizes documents and produces summaries. They will apply OCR (PyTesseract + OpenCV) to scan multi-page documents, then use NLP (text cleaning, summarization models) to generate concise overviews. This reinforces the full pipeline: upload PDF/DOCX, extract text, clean it, chunk content, and produce an automated summary.

  Module 10: Invoice and Receipt Processing Project – This hands-on module focuses on financial documents. Students will use OCR to extract text from invoice/receipt images and then apply NLP (regular expressions, spaCy models) to identify key fields (e.g. invoice number, dates, totals). They will learn structured data extraction: for instance, using PyTesseract to read line items and Pandas to parse tabular data, as demonstrated in industry use cases. By project end, students can automate invoice data capture.

  Module 11: Healthcare and Legal Document Applications – Learners explore domain-specific applications. In healthcare, students will apply OCR+NLP to medical reports (e.g. extracting patient info or test results with SciSpacy). In legal, they will process contracts and filings (e.g. parsing clauses, performing NER on legal entities) using spaCy and Transformers. This module covers how domain rules and specialized models improve accuracy. Emphasis is on using NLP to digitize and analyze records securely and efficiently.

  Module 12: Deploying OCR/NLP Solutions – The final module covers putting models into production. Students will build a simple Flask web API that runs OCR and NLP pipelines on uploaded documents. They will containerize the application with Docker, learning how Docker packages code and dependencies for portable deployment . If time permits, deployment on a cloud platform (e.g. AWS or Azure) will be demonstrated, illustrating end-to-end delivery of OCR/NLP services.