Digitizing Paper Data with Graphic Handwriting Recognition
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Solution Overview
Problem
Existing methods for digitizing paper data can only recognize character content and fail to recognize graphic content, making it difficult to convert paper data into digitized form when graphic information is present.
Innovation Solution
A method and apparatus that determine a standard template from the image of paper data, recognize graphic handwriting information using a semantic segmentation model, and generate digitized data by mapping the graphic information into the standard template, allowing for the inclusion of graphic content in the digitization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If character recognition methods are used for data digitization, then text content can be recognized, but graphic content cannot be recognized
Solution Approach 1:
The patent applies a semantic segmentation model that can simultaneously recognize both character content and graphic content in paper data. The model is designed to handle multiple types of information (text, graphs, charts, diagrams) within a single recognition system, making it universal rather than specialized for only one type of content. This resolves the contradiction by enabling the system to maintain high character recognition accuracy while also gaining the capability to recognize graphic content.
2Productivity
If paper data is converted to digitized form using existing methods, then text data can be digitized, but graphic data cannot be converted
Solution Approach 1:
The patent segments the paper data into different regions (character regions and graphic regions) using the semantic segmentation model. By dividing the recognition task into distinct segments - one for text and one for graphics - the system can process each type of information appropriately and convert both to digitized form without losing graphic information. This segmentation approach enables complete digitization of paper data including both text and graphic elements.
3Device complexity
If only character recognition is performed, then the process is simple, but graphic handwriting information cannot be recognized
Solution Approach 1:
The patent introduces a semantic segmentation model as an intermediary component between the input paper data and the digitization output. This intermediary model performs the complex task of distinguishing and segmenting different types of content (text vs. graphics) before further processing. By adding this intermediary layer, the system gains the ability to accurately recognize graphic handwriting information while maintaining a manageable overall system architecture.
Data Source
AI summary
The present application discloses a method and apparatus for digitizing paper data, an electronic device and a storage medium, relating to fields of image processing and cloud computing, in particular to image recognition technologies. The method includes: determining a standard template according to an image to be processed and mark information corresponding to the image to be processed, wherein the image to be processed is obtained by photographing paper data and the standard template is used to represent a reference coordinate system of the image to be processed; recognizing graphic handwriting information comprised in the image to be processed; and generating digitized data corresponding to the image to be processed according to the graphic handwriting information and the standard template.