one of the Codia AI Design technologies: Font Restoration Technology

1. Introduction

In the articles Codia AI: Shaping the Design and Code Revolution of 2024 and Codia AI: Shaping the Design and Code Revolution of 2024 - Part 2, Codia AI was introduced. This article will detail the font restoration technology in the feature of converting images to Figma design drafts by Codia AI Design.

2. Font Restoration Process Flowchart

Input Image
    |
    v
Image Preprocessing
    |-> Image Enhancement
    |-> Denoising
    |-> Binarization
    |-> Skew Correction
    |
    v
Text Recognition (OCR)
    |-> Text Area Detection
    |-> Character Segmentation
    |-> Character Recognition
    |
    v
Feature Extraction
    |-> Glyph Structure Analysis
    |-> Font Style Recognition
    |-> Typesetting Feature Extraction
    |
    v
Font Matching
    |-> Establishing Font Database
    |-> Similarity Calculation
    |-> Font Selection
    |
    v
Output to Figma
    |-> Apply Matched Font
    |-> Adjust Typesetting
    |-> User Confirmation/Manual Adjustment
    |
    v
Figma Design Draft

3. Image Preprocessing

Before text recognition, image preprocessing is an essential step. The purpose of preprocessing is to enhance the recognizability of text and reduce the interference of background noise. Common preprocessing operations include:

• Image Enhancement: Adjusting the image's contrast and brightness to make the text clearer.
• Denoising: Using filters to remove random noise from the image.
• Binarization: Converting the image to black and white to simplify subsequent processing.
• Skew Correction: Correcting the tilt of text in the image to ensure it is horizontal or vertical.

4. Text Recognition (OCR)

The preprocessed image will be input into the OCR system. OCR technology converts the text areas in the image into editable text by analyzing them. My previous article one of the Codia AI Design technologies: OCR Technology provides a detailed introduction to OCR technology, which typically includes the following steps:

• Text Area Detection: Locating text areas in the image.
• Character Segmentation: Segmenting the text within text areas into individual characters.
• Character Recognition: Recognizing each character and converting it into corresponding text.

5. Font Matching Technology

5.1. Feature Extraction

The first step in font matching is to extract font features from the text recognized by OCR. These features include:

• Glyph Structure: Analyzing the basic shapes of characters, such as rounded corners, sharp angles, stroke thickness, etc.
• Font Style: Identifying the style attributes of the text, such as italic, bold, underline, etc.
• Typesetting Features: Extracting typesetting features like letter spacing and line spacing.

5.2. Establishing a Font Database

To perform font matching, a database containing various font samples must be established. Each font sample has corresponding feature data, which can be manually annotated or generated through automated tools.

5.3. Similarity Calculation

Machine learning algorithms are used to calculate the similarity between the input text features and the features of each font sample in the database. These algorithms may include:

• Support Vector Machine (SVM): A supervised learning method suitable for classification problems in feature space.
• Convolutional Neural Network (CNN): A deep learning model that excels at processing image data.
• Deep Learning Models: Such as Recurrent Neural Networks (RNN) or Transformers, which can handle sequential data and are suitable for the contextual analysis of text.

5.4. Font Selection

Based on the similarity calculation results, the most matching font is selected and applied to the Figma design draft. If there are multiple candidate fonts, weights or other criteria may be used to determine the final selection.

6. Pseudocode

# Import the necessary libraries
import ocr_library
import image_processing_library
import font_matching_library
import figma_api_library

def process_image_to_figma(input_image_path):
    # Image preprocessing
    processed_image = image_processing_library.preprocess_image(input_image_path)

    # Text recognition (OCR)
    text_blocks = ocr_library.detect_text(processed_image)

    # Feature extraction and font matching
    figma_elements = []
    for block in text_blocks:
        text = ocr_library.recognize_text(block)
        font_features = font_matching_library.extract_font_features(block)
        matched_font = font_matching_library.match_font(font_features)

        # Create Figma element
        figma_element = figma_api_library.create_text_element(text, matched_font)
        figma_elements.append(figma_element)

    # Output to Figma
    figma_design = figma_api_library.create_figma_design(figma_elements)

    return figma_design

# Use the function to process the image and generate a Figma design draft
figma_design = process_image_to_figma("path_to_input_image.jpg")

7. Conclusion

Through this article, we have learned about the process of converting text and design elements from images into Figma design drafts. From image preprocessing to text recognition (OCR), and then to font feature extraction and matching, each step is key to ensuring the accuracy of the design draft. We also discussed how to establish a font database and how to use machine learning algorithms to calculate similarity and select the most matching font. Finally, we demonstrated the automation of this process through pseudocode, enabling designers to more efficiently convert images to Figma design drafts, thereby saving time, reducing repetitive labor, and focusing on creative design work. As technology continues to advance, we look forward to these tools and methods evolving further, providing more support to designers and driving the design industry forward.