Image Text Font Recognition for Editable Document Conversion
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Solution Overview
Problem
Existing image editing tools are inefficient for converting image-based text into editable text, lacking the ability to accurately recognize and convert font styles and requiring cumbersome manual editing.
Innovation Solution
A method using trained machine learning models to analyze image crops, determine font characteristics, and create editable documents by inpainting original text with editable text in matching fonts, employing convolutional neural networks and multi-layer perceptron networks for accurate font recognition and inpainting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing image editing tools are used to convert image-based text into editable text, then the conversion process can be performed, but the efficiency is low and manual editing effort is high
Solution Approach 1:
The patent replaces manual mechanical text extraction and editing processes with automated machine learning models. The system uses trained ML models to automatically recognize text in images, determine font characteristics, and generate editable text documents, eliminating the need for manual image editing and significantly improving conversion efficiency while reducing time loss
Solution Approach 2:
The system enables self-service text conversion by automatically processing images through multiple ML models that perform text recognition, font identification, and document generation without human intervention. The automated pipeline handles the entire conversion process from image input to editable text output, making the system self-sufficient and highly productive
2Measurement precision
If existing image editing tools are used to recognize and convert font styles, then text conversion can be performed, but the accuracy of font recognition is insufficient
Solution Approach 1:
The patent segments the font recognition task into multiple specialized machine learning models, each trained to identify specific font characteristics. This segmentation allows the system to achieve high accuracy in font recognition by dividing the complex task into manageable components that can be processed independently and then combined
Solution Approach 2:
The system changes parameters by using multiple trained ML models with different specialization to analyze various aspects of font characteristics. Each model evaluates specific font parameters, and their combined results achieve high precision in font identification and text conversion
3Ease of operation
If manual text editing is performed on image-based text, then text can be edited, but the process is cumbersome and inefficient
Solution Approach 1:
The patent replaces cumbersome manual text editing operations with automated machine learning-based text extraction and document generation. The system automatically converts image-based text into editable text formats, eliminating the need for manual image editing operations and making the process both easier and more efficient
Solution Approach 2:
The system creates accurate copies of the original text from the image by using ML models to recognize and reproduce the text content in editable formats. This copying process preserves the original text while making it editable, eliminating the need for manual retyping and significantly improving ease of operation and productivity
Data Source
AI summary
Image processing techniques are described, including techniques in which text data associated with an image is used to determine a font of text in an image. The image is split into a plurality of crops based on the text data. A trained machine learning model is used to determine feature vectors of the image. The feature vectors are combined into a combined feature vector. A second trained machine learning model is used to determine a font using the combined feature vector. The second trained machine learning model may be a multi-layer perceptron network. The second trained machined learning model may be trained on a plurality of images with text of known fonts and properties. The described image processing techniques also include text removal.