Chinese Character Recognition Based on MATLAB [GUI Interface + Research Paper]

The MATLAB algorithm workflow is as follows:

1. Input image processing: Load and preprocess the input image using MATLAB's imread() function for image acquisition.

2. Grayscale conversion: Convert color images to grayscale using rgb2gray() function to reduce computational complexity.

3. Image enhancement: Apply histogram equalization (histeq()) or contrast adjustment (imadjust()) to improve image quality and feature visibility.

4. Edge detection: Implement edge detection algorithms such as Canny or Sobel using edge() function to identify character boundaries.

5. Erosion operation: Perform morphological erosion using imerode() to remove small noise and separate connected characters.

6. Closing operation: Apply morphological closing (imdilate() followed by imerode()) to fill small holes and connect broken character strokes.

7. Small-area noise removal: Use regionprops() or bwareaopen() to filter out small connected components that don't represent valid characters.

8. Obtain located Chinese character regions: Generate bounding boxes around detected character regions using bounding box calculations.

9. Character segmentation using connected component analysis: Implement bwconncomp() or regionprops() to segment individual characters, similar to license plate recognition techniques.

10. Template matching recognition: Compare segmented characters with template database using correlation-based matching (normxcorr2()) or feature-based classification methods.

The above describes the complete MATLAB algorithm workflow for Chinese character recognition.