Video Stream Text Recognition With Neural Stop Prediction
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Solution Overview
Problem
The challenge in video stream text recognition is determining the optimal stopping moment without relying on the assumption of a monotone problem, as existing methods struggle with variable lighting and camera conditions, lack of open datasets, and the need for accurate and efficient recognition of identity documents.
Innovation Solution
A predictive model, such as a neural network, is used to classify the stopping moment by calculating errors between combined text-recognition results and applying a multilayer perceptron to classify whether to continue or stop the recognition process based on accumulated feature sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text recognition is performed on multiple frames in a video stream to improve accuracy, then recognition accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies dynamics by making the stopping criterion adaptive rather than fixed. The recognition process dynamically adjusts when to stop based on real-time assessment of result stability, using metrics like character error rate changes between frames to determine the optimal stopping point, thereby balancing accuracy improvement against processing time consumption
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring the stability of recognition results across frames and using this information to control the stopping decision. The system feeds back the change in character error rates and result consistency to the stopping criterion, which then adjusts the recognition process termination accordingly
2Measurement precision
If the recognition process continues for more frames to capture better quality data, then recognition accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent applies preliminary action by establishing stopping criteria and quality thresholds before the recognition process begins. These pre-defined conditions (such as minimum frame quality requirements and error rate thresholds) guide the recognition process to stop early when conditions are met, avoiding unnecessary processing and resource consumption while ensuring adequate accuracy
3Productivity
If a fixed number of frames is used for recognition, then processing efficiency is improved, but recognition accuracy deteriorates under variable conditions
Solution Approach 1:
The patent replaces fixed frame counts with dynamic stopping criteria that adapt to varying video conditions. The system continuously evaluates recognition result stability and quality metrics, adjusting the number of frames processed based on actual performance rather than a predetermined limit, thus maintaining efficiency while improving accuracy under variable conditions
Data Source
AI summary
When performing text recognition on a video stream, it is beneficial to stop the recognition process as soon as an acceptable recognition result is obtained. In an embodiment, a method for determining when to stop the recognition process method for a text field utilizes a predictive model, such as a neural network, to classify a sequence of calculated errors between text-recognition results for a set of successive frames in the video stream. The classification is used to determine whether to stop the recognition process at the current frame, or to continue the recognition process on the next frame.