Dynamic Image Character Recognition via Weighted Frame Accumulation
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Solution Overview
Problem
Dynamic image character recognition systems face challenges with high resource consumption, blurry images due to fast object movement, and reduced accuracy from non-ideal camera positions, along with difficulties in determining the correct recognition result from multiple outputs.
Innovation Solution
A character recognition method that detects and tracks a target object across consecutive images by calculating a weighting score based on object area ratio, position, and recognition results, accumulating scores until a preset value is reached, and outputs the result only when it exceeds this threshold, thereby optimizing resource usage and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic image capture is performed at high frame rates to improve recognition success rate, then recognition accuracy is improved, but system resource consumption increases
Solution Approach 1:
The patent applies partial action by performing character recognition on only selected frames rather than all captured frames. The system identifies key frames where the target object meets specific criteria (such as being within a certain distance range or occupying a appropriate area ratio in the image) and performs recognition only on these frames, thereby reducing overall computational resource consumption while maintaining recognition accuracy.
Solution Approach 2:
The patent extracts and processes only the essential information from captured frames. By identifying and focusing on key frames that contain the target object in optimal conditions, the system extracts useful recognition data while discarding redundant frames, thus reducing resource consumption without sacrificing recognition success rate.
2Speed
If the target object moves fast causing blurry images, then capture speed is improved, but recognition accuracy deteriorates
Solution Approach 1:
The patent employs feedback mechanisms by evaluating image quality metrics such as area ratio and clarity of the target object in each captured frame. Based on this feedback, the system determines whether the current frame is suitable for recognition. If the target object is too blurry or not properly framed, the system skips recognition for that frame and continues capturing, thereby maintaining accuracy despite high-speed movement.
3Reliability
If multiple recognition results are obtained from consecutive images, then recognition success rate is improved, but difficulty in determining correct result increases
Solution Approach 1:
The patent introduces an intermediary validation mechanism that acts as a mediator between multiple recognition results and the final output. This validation process checks whether consecutive recognition results are consistent with each other and meet predetermined criteria before accepting them as correct. The intermediary layer simplifies the complexity by providing a systematic approach to validate and select the correct result from multiple candidates.
4Adaptability or versatility
If camera is not placed directly in front of target object to avoid obstruction, then capture flexibility is improved, but recognition accuracy deteriorates
Solution Approach 1:
The patent compensates for non-ideal camera positions by dynamically adjusting recognition parameters based on the detected geometry and orientation of the target object. When the camera views the target from an angle, the system modifies recognition thresholds and parameters to account for perspective distortion and varying apparent sizes, thereby maintaining recognition accuracy despite reduced capture flexibility constraints.
Data Source
AI summary
A character recognition method includes the stages as detailed in the following paragraph. An image is received, wherein the image is one in a plurality of consecutive images. A target object in the image is detected. Object information of the target object is defined according to the area ratio of the target object occupied in the image. Whether the target object in the image is the same as the target object in the previous image is determined according to the object information. Character recognition on the target object is performed to obtain a recognition result. The weighting score of the recognition result is calculated according to the object information and the recognition result. The weighting score of the recognition result of the target object in the consecutive images is accumulated until the weighting score is higher than a preset value, and the recognition result is output.