Automated Truncated Text Detection in GUI Localization
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Solution Overview
Problem
During software localization, character strings associated with screen controls in graphical user interfaces may be truncated due to lengthening of text without corresponding redesign of the screen controls or their environments, leading to incomplete or obscured text, which can be difficult to detect manually, especially when adapted for different languages or cultures.
Innovation Solution
An automatic detection system using optical character recognition (OCR) compares the displayed text with designated character strings from a control text table to identify truncations, issuing alerts and allowing for corrective actions to ensure accurate text display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If text is lengthened during localization to improve accuracy and cultural adaptation, then text completeness is improved, but screen control display space is exceeded causing truncation
Solution Approach 1:
The system performs preliminary detection of text truncation by comparing OCR-recognized displayed text with the original character strings from the control text table before finalizing the localized interface. This allows proactive identification and correction of truncation issues before they affect end users.
Solution Approach 2:
The system establishes a feedback loop where the displayed text is automatically recognized via OCR, compared with the original character strings, and any discrepancies indicating truncation are reported. This feedback mechanism enables continuous verification and correction of text display integrity during localization validation.
2Measurement precision
If manual detection of truncated text is used, then detection accuracy may be high, but time consumption and labor cost increase significantly
Solution Approach 1:
The system replaces manual visual inspection with automated optical character recognition (OCR) technology. The OCR engine captures and recognizes text from screenshots of the localized interface, automatically comparing it with the original character strings to detect truncation, thereby eliminating time-consuming manual detection while maintaining high accuracy.
Solution Approach 2:
The system creates a digital copy of the displayed text through OCR recognition and compares it with the original character string copy from the control text table. This copying and comparison approach enables automated detection without requiring manual reading and comparison, significantly reducing detection time while preserving accuracy.
3Ease of operation
If screen control layout is redesigned to accommodate longer localized text, then text display completeness is improved, but device complexity and development cost increase
Solution Approach 1:
The system enables self-service validation of text display integrity through automated OCR-based detection. Instead of requiring developers to manually redesign screen controls for each localization, the system automatically detects truncation issues and provides feedback, allowing developers to make informed decisions about whether redesign is necessary without adding unnecessary complexity.
Solution Approach 2:
The system performs partial validation by focusing specifically on detecting text truncation rather than comprehensive interface redesign. This selective approach allows localization teams to identify problems without immediately committing to full screen control redesign, enabling more efficient resource allocation and reducing unnecessary development complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system efficiently detects and addresses truncated character strings, improving the accuracy and efficiency of graphical user interface adaptation by automatically identifying and correcting text truncations, thus ensuring proper display and usability across different locales.
Implementation Method 1
An automatic detection system using optical character recognition (OCR) compares the displayed text with designated character strings
Data Source
AI summary
A method includes automatically identifying a screen control in a user interface. Optical character recognition is applied to read a text that is displayed on an image of the screen control. The displayed text is automatically compared to a character string that is designated for the screen control. If part of the character string is not included in the displayed text, the displayed text is identified as truncated text.