Handheld Scanner Training for Alternate Font Recognition
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Solution Overview
Problem
Current handheld scanners are limited to scanning standardized machine readable fonts like OCR-A and OCR-B and lack the capability to be trained on alternate fonts or symbols due to insufficient processing power and user interface, making it difficult to achieve quality scans in various non-typical reading conditions.
Innovation Solution
Incorporating vision software and a user interface with a range finder into handheld scanners to enable training on alternate fonts and symbols, allowing users to associate images with electronic data and generate font description files for improved decoding capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If handheld scanners use standardized machine readable fonts like OCR-A and OCR-B, then scanning accuracy is improved, but adaptability to alternate fonts and symbols deteriorates
Solution Approach 1:
The system performs preliminary training by collecting sample images of alternate fonts and symbols, associating them with electronic data, and generating font description files before actual scanning operations. This pre-training process enables the scanner to recognize non-standard fonts without sacrificing accuracy in production scanning.
Solution Approach 2:
The system changes the parameters of font recognition by transforming raw image data into structured font description files that contain characteristic features of each font. This parameter transformation allows the scanner to adapt to different font styles while maintaining consistent scanning accuracy through the standardized font description format.
2Adaptability or versatility
If handheld scanners incorporate training capability for alternate fonts, then adaptability is improved, but device complexity deteriorates
Solution Approach 1:
The training functionality is segmented into separate components: image collection, association with electronic data, and font description file generation. This modular approach allows the complex training process to be broken down into manageable tasks that can be executed by the handheld scanner's existing processing capabilities without requiring a complete system redesign.
Solution Approach 2:
The system creates simplified copies of font characteristics in the form of font description files, which are lightweight data structures that capture the essential features of each font. These copied representations can be stored and processed efficiently, enabling adaptability without proportionally increasing device complexity.
3Device complexity
If current handheld scanners are used with limited OCR functionality, then device simplicity is maintained, but scanning quality for non-typical reading conditions deteriorates
Solution Approach 1:
The system introduces dynamic adaptability by allowing the scanner to learn and adjust to different reading conditions through the training process. The font description files serve as adaptive parameters that enable the scanner to optimize its recognition performance for specific non-typical reading conditions while maintaining the same simple device hardware.
Data Source
AI summary
A handheld scanner incorporates vision software to allow the handheld scanner to be trained for OCR. The handheld scanner can include a user interface to allow a user to associate an image of a mark with electronic data for the mark. The user interface, along with a range finder, can also be used to influence variables that affect the quality of an image scan, thereby improving the quality of results for the image scan and/or decode process.