OCR – Text Detection and Recognition
One of the most powerful applications of Computer Vision is the ability to read text from images.
This capability is called Optical Character Recognition (OCR), and it bridges the gap between visual data and language data.
In this lesson, you will understand how OCR systems work, what problems they solve, and why OCR is more complex than it looks.
What Is OCR?
OCR is the process of:
- Detecting text regions in an image
- Recognizing characters inside those regions
- Converting them into machine-readable text
In simple words:
Image → Text
Why OCR Is a Computer Vision Problem
Although OCR outputs text, the input is still an image.
The system must deal with:
- Different fonts
- Different sizes
- Lighting variations
- Background noise
- Skewed or rotated text
This makes OCR fundamentally a vision problem, not just language.
Two Core Stages of OCR
Modern OCR systems are built using two major stages:
- Text Detection
- Text Recognition
Both stages are equally important.
Stage 1: Text Detection
Text detection answers the question:
Where is the text in the image?
The model outputs bounding boxes or polygons around text regions.
Common detection techniques:
- Traditional image processing (older)
- EAST text detector
- CTPN
- YOLO-based text detectors
At this stage, the model does not know what the text says.
Stage 2: Text Recognition
Once text regions are detected, recognition begins.
This stage answers:
What does the text say?
Each detected text region is cropped and passed to a recognition model.
Recognition models typically use:
- CNNs for visual features
- RNNs or Transformers for sequence modeling
- CTC loss for alignment
Why OCR Is Harder Than Face Detection
Faces have relatively fixed structure.
Text does not.
- Variable length
- Different scripts
- Curved or handwritten text
OCR models must generalize far more.
OCR Pipeline (End-to-End)
Image → Preprocessing → Text Detection → Text Cropping → Text Recognition → Post-processing
Each step improves final accuracy.
Preprocessing in OCR
Before detection, images are often preprocessed:
- Grayscale conversion
- Noise removal
- Contrast enhancement
- Thresholding
Good preprocessing can dramatically improve OCR results.
Types of OCR Systems
OCR systems can be categorized by usage:
- Printed OCR – books, documents
- Handwritten OCR – forms, notes
- Scene Text OCR – street signs, boards
Scene text OCR is the hardest.
Real-World OCR Applications
- Document digitization
- Invoice processing
- ID and passport scanning
- License plate recognition
- Searchable PDFs
OCR is a backbone of automation systems.
OCR vs Simple Image-to-Text
OCR is not just reading pixels.
It must understand:
- Character boundaries
- Word order
- Language constraints
That is why deep learning is essential.
Limitations of OCR
OCR still struggles with:
- Very low-resolution images
- Heavy blur
- Stylized fonts
- Extreme lighting conditions
Human-level OCR is still an open challenge.
Practice Questions
Q1. What are the two main stages of OCR?
Q2. Why is OCR harder than object detection?
Q3. What is the role of preprocessing in OCR?
Mini Assignment
Imagine scanning a damaged receipt.
- Which OCR stage fails first?
- How could preprocessing help?
Answer conceptually.
Quick Recap
- OCR converts images into text
- Consists of detection and recognition
- Uses CNNs, RNNs, Transformers
- Critical for automation
- Still a challenging CV problem
Next lesson: Face Recognition.