agirishkumar/cudaocr

Building an OCR engine using CUDA

Enhanced CUDA-Accelerated OCR Pipeline for Printed English Text

Issues

• some issue with preprocessed image, too much noise, bottom is blacked. check the sample.png and preprocessed_sample.png
• tried to use texture memory for blurring, median flitering but failed, so removed it. should try later

Plan of Action:

• Create Logger
• Create Log file
• Create Makefile
• Organize the project file structure
• Supress the warnings

1. Image Acquisition

• Load image from file or capture from camera
• Transfer image to GPU memory
• Implement robust error handling for different image formats
• Add image quality assessment to filter out low-quality images early
• Use CUDA streams for asynchronous data transfer when processing multiple images (==for later==)

2. Preprocessing (GPU)

• Utilize NVIDIA Performance Primitives (NPP) for efficient image processing (==later if required==)
• Implement parameter tuning for each step (e.g., kernel size, thresholds)

1. Color to Grayscale Conversion
   • Average method
   • Luminosity Method
   • Desaturation Method
2. Image Denoising
   • Apply Gaussian blur
   • median filter
3. Contrast Enhancement
   • Implement adaptive histogram equalization
   • Implement CLAHE
4. Binarization
   • Implement Otsu's thresholding
   • Implement adaptive thresholding

3. Page Layout Analysis (GPU)

• Use cuCIM library for faster processing (==Optional==)
• Implement methods to handle various document layouts (e.g., multi-column) (==for later==)

1. Skew Detection and Correction
   • Calculate the skew and correct the rotation
2. Document Structure Analysis
   • Identify text blocks, images, tables, etc.

4. Text Line Detection (GPU)

1. Advanced Morphological Operations
   • Handle diverse fonts and text sizes
2. Connected Component Analysis
   • Implement the CCA
3. Text Line Extraction
   • Group connected components into text lines

• Investigate deep learning-based approaches for more accurate detection(==later==)

5. Word Segmentation (GPU)

1. Inter-word Space Detection
   • Implement edge detection methods
2. Word Bounding Box Extraction
   • Use DBSCAN clustering for better word grouping

6. Character Segmentation (GPU)

1. Vertical Projection Analysis
2. Character Bounding Box Extraction

• Implement techniques to handle touching or overlapping characters

7. Feature Extraction (GPU)

1. Character Normalization
   • Resize and center each character
2. Feature Computation
   • Experiment with various techniques (e.g., HOG, pixel intensity patterns)
   • Ensure robustness to font style and size variations

8. Character Recognition (GPU with cuDNN)

• Evaluate different models (CNN, LSTM) for optimal accuracy and speed
• Use transfer learning with pre-trained models
• Implement model quantization for faster inference

9. Post-processing

1. Language Model Application (GPU/CPU)
   • Use advanced models like BERT or GPT for context understanding
2. Word Formation and Validation
3. Text Line Formation

• Implement a feedback loop to refine earlier stages based on language model output

10. Output Generation

1. Text Formatting
   • Match original layout
2. Result Visualization
   • Highlight recognized text on the original image
3. Multi-format Output
   • Support various formats (e.g., JSON, PDF) with metadata

11. Quality Assurance

1. Confidence Scoring
2. Error Detection and Correction
3. User Feedback Mechanism
   • Continuously improve OCR accuracy based on corrections

12. User Interface (Optional)

1. Responsive Input Interface
2. Interactive Result Display
3. Manual Correction Tools
4. Accessibility Features

Additional Considerations

• Benchmarking: Continuously profile and benchmark each stage
• Parallelization: Optimize pipeline to fully utilize GPU capabilities
• Modularization: Develop each stage as an independent, easily updatable component
• Error Handling: Implement robust error management throughout the pipeline
• Scalability: Design the system to handle varying workloads efficiently
• Data Augmentation: For training and testing, augment data to improve robustness
• Version Control: Use Git for tracking changes and collaborating
• Documentation: Maintain comprehensive documentation for each module
• Testing: Implement unit tests and integration tests for each component

To run the program:

clone the repo

gh repo clone agirishkumar/CudaOCR
cd CudaOCR
make
./app

This project is making me go crazy... fucked my sleep cycle 🥲.. but its fun!!