Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives
This work addresses an incremental improvement in stopping methods for text recognition in video streams, primarily benefiting applications in document analysis and mobile device recognition.
The paper tackles the problem of determining when to stop capturing video for text field recognition to optimize both time and accuracy, proposing an extension of a stopping method based on next integrated result modeling with per-character alternatives. Experimental results on the MIDV-500 dataset show that this method achieves higher accuracy compared to previous clustering-based methods, even against their best configurations.
In the field of document analysis and recognition using mobile devices for capturing, and the field of object recognition in a video stream, an important problem is determining the time when the capturing process should be stopped. Efficient stopping influences not only the total time spent for performing recognition and data entry, but the expected accuracy of the result as well. This paper is directed on extending the stopping method based on next integrated recognition result modelling, in order for it to be used within a string result recognition model with per-character alternatives. The stopping method and notes on its extension are described, and experimental evaluation is performed on an open dataset MIDV-500. The method was compares with previously published methods based on input observations clustering. The obtained results indicate that the stopping method based on the next integrated result modelling allows to achieve higher accuracy, even when compared with the best achievable configuration of the competing methods.