Optical character recognition (OCR)

Optical Character Recognition (OCR) is a technology that automatically converts visual text in images - whether from scanned documents, photographs, or digital files—into editable, searchable text data. Rather than manually transcribing information, OCR enables automated data extraction from:

• Scanned invoices and receipts
• Digital photographs of documents
• PDF files containing images of text
• Screenshots and captured content
• Forms and handwritten notes

The OCR pipeline: A step-by-step process

The OCR pipeline consists of five essential stages that work together to transform visual information into text data.

The stages in the OCR process are:

1. Image acquisition and input.
2. Preprocessing and image enhancement.
3. Text region detection.
4. Character recognition and classification.
5. Output generation and post-processing.

Let's examine each stage in more depth.

Stage 1: Image acquisition and input

The pipeline begins when an image containing text enters the system. This could be:

• A photograph taken with a smartphone camera.
• A scanned document from a flatbed or document scanner.
• A frame extracted from a video stream.
• A PDF page rendered as an image.

Stage 2: Preprocessing and image enhancement

Before text detection begins, the following techniques are used to optimize the image for better recognition accuracy:

• Noise reduction removes visual artifacts, dust spots, and scanning imperfections that could interfere with text detection. The specific techniques used to perform noise reduction include:

  • Filtering and image processing algorithms: Gaussian filters, median filters, and morphological operations.
  • Machine learning models: Denoising autoencoders and convolutional neural networks (CNNs) trained specifically for document image cleanup.

• Contrast adjustment enhances the difference between text and background to make characters more distinct. Again, there are multiple possible approaches:

  • Classical methods: Histogram equalization, adaptive thresholding, and gamma correction.
  • Machine learning: Deep learning models that learn optimal enhancement parameters for different document types.

• Skew correction detects and corrects document rotation, ensuring text lines are properly aligned horizontally. Techniques for skew correction include:

  • Mathematical techniques: Hough transform for line detection, projection profiles, and connected component analysis.
  • Neural network models: Regression CNNs that predict rotation angles directly from image features.

• Resolution optimization adjusts image resolution to the optimal level for character recognition algorithms. You can optimize image resolution with:

  • Interpolation methods: Bicubic, bilinear, and Lanczos resampling algorithms.
  • Super-resolution models: Generative adversarial networks (GANs) and residual networks that intelligently upscale low-resolution text images.

Stage 3: Text region detection

The system analyzes the preprocessed image to identify areas that contain text by using the following techniques:

• Layout analysis distinguishes between text regions, images, graphics, and white space areas. Techniques for layout analysis include:

  • Traditional approaches: Connected component analysis, run-length encoding, and projection-based segmentation.
  • Deep learning models: Semantic segmentation networks like U-Net, Mask R-CNN, and specialized document layout analysis models (for example, LayoutLM, or PubLayNet-trained models).

• Text block identification groups individual characters into words, lines, and paragraphs based on spatial relationships. Common approaches include:

  • Classical methods: Distance-based clustering, white space analysis, and morphological operations
  • Neural networks: Graph neural networks and transformer models that understand spatial document structure

• Reading order determination establishes the sequence in which text should be read (left-to-right, top-to-bottom for English). The correct order can be determined by:

  • Rule-based systems: Geometric algorithms using bounding box coordinates and spatial heuristics.
  • Machine learning models: Sequence prediction models and graph-based approaches that learn reading patterns from training data.

• Region classification identifies different types of text regions (headers, body text, captions, tables).

  • Feature-based classifiers: Support vector machines (SVMs) using handcrafted features like font size, position, and formatting
  • Deep learning models: Convolutional neural networks and vision transformers trained on labeled document datasets

Stage 4: Character recognition and classification

This is the core of the OCR process where individual characters are identified:

• Feature extraction: Analyzes the shape, size, and distinctive characteristics of each character or symbol.

  • Traditional methods: Statistical features like moments, Fourier descriptors, and structural features (loops, endpoints, intersections)
  • Deep learning approaches: Convolutional neural networks that automatically learn discriminative features from raw pixel data

• Pattern matching: Compares extracted features against trained models that recognize different fonts, sizes, and writing styles.

  • Template matching: Direct comparison with stored character templates using correlation techniques
  • Statistical classifiers: Hidden Markov Models (HMMs), Support Vector Machines, and k-nearest neighbors using feature vectors
  • Neural networks: Multi-layer perceptrons, CNNs, and specialized architectures like LeNet for digit recognition
  • Advanced deep learning: Residual networks (ResNet), DenseNet, and EfficientNet architectures for robust character classification

• Context analysis: Uses surrounding characters and words to improve recognition accuracy through dictionary lookups and language models.

  • N-gram models: Statistical language models that predict character sequences based on probability distributions.
  • Dictionary-based correction: Lexicon lookup with edit distance algorithms (such as Levenshtein distance) for spelling correction.
  • Neural language models: LSTM and transformer-based models (like BERT variants) that understand contextual relationships.
  • Attention mechanisms: Transformer models that focus on relevant parts of the input when making character predictions.

• Confidence scoring: Assigns probability scores to each recognized character based on how certain the system is about its identification.

  • Bayesian approaches: Probabilistic models that quantify uncertainty in character predictions.
  • Softmax outputs: Neural network final layer activations converted to probability distributions.
  • Ensemble methods: Combining predictions from multiple models to improve confidence estimates.

Stage 5: Output generation and post-processing

The final stage converts recognition results into usable text data:

• Text compilation: Assembles individual character recognitions into complete words and sentences.

  • Rule-based assembly: Deterministic algorithms that combine character predictions using spatial proximity and confidence thresholds.
  • Sequence models: Recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks that model text as sequential data.
  • Attention-based models: Transformer architectures that can handle variable-length sequences and complex text layouts.

• Format preservation: Maintains document structure including paragraphs, line breaks, and spacing.

  • Geometric algorithms: Rule-based systems using bounding box coordinates and white space analysis.
  • Layout understanding models: Graph neural networks and document AI models that learn structural relationships.
  • Multi-modal transformers: Models like LayoutLM that combine text and layout information for structure preservation.

• Coordinate mapping: Records the exact position of each text element within the original image.

  • Coordinate transformation: Mathematical mapping between image pixels and document coordinates.
  • Spatial indexing: Data structures like R-trees and quad-trees for efficient spatial queries.
  • Regression models: Neural networks trained to predict precise text positioning coordinates.

• Quality validation: Applies spelling and grammar checks to identify potential recognition errors.

  • Dictionary-based validation: Lookup against comprehensive word lists and specialized domain vocabularies.
  • Statistical language models: N-gram models and probabilistic parsers for grammar and context validation.
  • Neural language models: Pre-trained models like GPT or BERT fine-tuned for OCR error detection and correction.
  • Ensemble validation: Combining multiple validation approaches to improve error detection accuracy.