Digitized Handwriting Ingestion via Stroke Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing personalized-handwriting fonts and creation services struggle to accurately replicate the unique geometry of individual handwritten characters, often appearing mechanical and unnatural, as they fail to isolate and digitize connected letters in cursive handwriting.
Innovation Solution
A client/server-based system for digitized handwriting data collection and analysis, utilizing a network of client and server devices to ingest and process handwriting samples, breaking down characters into code-points and recording stroke data to generate authentic-looking handwritten text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing personalized-handwriting fonts are used to mimic a person's handwriting, then a font can be created to replicate handwriting style, but the result appears mechanical and unnatural because individual glyphs are printed with identical geometry
Solution Approach 1:
The handwriting sample is segmented into individual glyph instances, where each glyph is further divided into stroke components. This segmentation allows the system to capture multiple geometric variations of each character rather than treating it as a single uniform glyph, thereby resolving the contradiction between ease of font creation and preservation of natural geometric variation.
Solution Approach 2:
The system transforms static glyph geometry into dynamic stroke-based representations with multiple dimensional values. Each glyph is represented as a sequence of strokes with varying geometric properties, allowing the font to dynamically generate natural variations when rendering text, thus achieving both ease of manufacture and manufacturing precision.
2Adaptability or versatility
If services create a font to mimic a particular person's handwriting, then personalized handwriting replication is achieved, but the system has difficulty isolating individual glyphs within cursive handwriting or connected letters
Solution Approach 1:
The system segments the continuous handwriting sample into discrete glyph instances by detecting stroke termination points and gaps between characters. This segmentation strategy enables the isolation of individual glyphs even in cursive writing where letters are connected, resolving the contradiction between adaptability and detection difficulty.
Solution Approach 2:
The system introduces stroke data as an intermediary representation between the raw handwriting image and the final glyph extraction. By analyzing stroke connectivity and spatial relationships, the system can effectively separate connected letters and isolate individual glyphs, thereby achieving both versatility in style replication and improved glyph detection.
3Manufacturing precision
If digitized handwriting samples are collected to capture unique character geometry, then natural handwriting appearance can be achieved, but the process requires complex client/server-based systems for data ingestion and processing
Solution Approach 1:
The system creates digital copies of handwriting samples in a standardized stroke-based format. By copying the essential geometric and temporal characteristics of handwriting into a structured data representation, the system achieves high manufacturing precision while simplifying the overall process through automated digitization rather than manual reconstruction.
Solution Approach 2:
The client/server system is designed with universal components that can handle multiple handwriting samples, different writing styles, and various output formats. This multi-functionality reduces device complexity by using a single versatile platform rather than requiring separate specialized systems for each handwriting digitization task.
Data Source
AI summary
Certain aspects of the present methods and systems may focus on computer implemented methods of obtaining digitized hand-writing data corresponding to a sample of a needed code point of a set of code points. Such methods may include: obtaining a sample of digitized handwritten text, the sample of digitized handwritten text including glyph data corresponding to a first glyph, the first glyph corresponding to the needed code point of the set of code points; associating the first glyph with the needed code point; identifying stroke data in the glyph data, the stroke data corresponding to a stroke component of the first glyph, determining a plurality of dimensional values of the stroke component in the stroke data; and associating the plurality of dimensional values with a new code point sample of the needed code point in a code point set data structure.