AI Lesson 90 – OCR & Text Extraction | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 90: OCR & Text Extraction

OCR stands for Optical Character Recognition. It is a computer vision technique that allows machines to read text from images, scanned documents, photos, and screenshots.

OCR converts visual text into machine-readable text so it can be stored, searched, edited, and analyzed like normal digital text.

Real-World Connection

OCR is used everywhere in daily life. When you scan a document using your phone, extract text from a receipt, search text inside a PDF, or digitize old books, OCR is working behind the scenes.

Banks, governments, logistics companies, and healthcare systems rely heavily on OCR to automate paperwork and reduce manual data entry.

What Is OCR?

OCR is the process of detecting characters in an image and converting them into text characters such as letters, numbers, and symbols.

  • Input: Image containing text
  • Process: Detect and recognize characters
  • Output: Editable digital text

How OCR Works

A typical OCR pipeline consists of several steps:

  • Image preprocessing (grayscale, noise removal)
  • Text region detection
  • Character segmentation
  • Character recognition
  • Post-processing and correction

Modern OCR systems use deep learning models that recognize entire words and lines instead of individual characters.

Popular OCR Tools and Libraries

  • Tesseract: Open-source OCR engine
  • EasyOCR: Deep learning–based OCR
  • Google Vision OCR: Cloud-based OCR API

In this lesson, we will use Tesseract because it is widely used and free.

OCR Using Tesseract (Code Example)

The following example shows how to extract text from an image using Python and Tesseract. 


import cv2
import pytesseract

image = cv2.imread("sample_text.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

text = pytesseract.image_to_string(gray)

print(text)

What This Code Is Doing

The image is first converted to grayscale to improve text clarity. The OCR engine then scans the image and recognizes text patterns.

The recognized text is returned as a normal Python string that can be printed, stored, or processed further.

Understanding the Output

The output will be the text detected inside the image. The accuracy depends on image quality, font style, lighting, and resolution.

Clear, high-contrast images usually produce the best OCR results.

Improving OCR Accuracy

  • Use high-resolution images
  • Apply noise removal and thresholding
  • Ensure proper text alignment
  • Choose the correct language model

Preprocessing the image often makes a significant difference in OCR accuracy.

OCR Use Cases

  • Document digitization
  • Invoice and receipt processing
  • License plate recognition
  • Form data extraction

Practice Questions

Practice 1: What does OCR stand for?



Practice 2: OCR extracts text from what type of input?



Practice 3: What step improves OCR accuracy before recognition?



Quick Quiz

Quiz 1: Which open-source OCR engine was used in the example?





Quiz 2: What is the final output of OCR?





Quiz 3: OCR is most useful for which task?





Coming up next: Image Augmentation Techniques — improving model performance using synthetic data variations.

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