Fast Approximate Modelling of the Next Combination Result for Stopping the Text Recognition in a Video

TL;DR

This paper introduces a fast approximation method for deciding when to stop recognizing text in video streams, reducing computation time without sacrificing accuracy, applicable to document and arbitrary text recognition tasks.

Contribution

It presents a novel approximation approach for the next combination result modeling, significantly decreasing computational complexity in video text recognition stopping decisions.

Findings

Approximate method maintains recognition quality.

Substantial reduction in stopping decision time.

Consistent results across different text recognition tasks.

Abstract

In this paper, we consider a task of stopping the video stream recognition process of a text field, in which each frame is recognized independently and the individual results are combined together. The video stream recognition stopping problem is an under-researched topic with regards to computer vision, but its relevance for building high-performance video recognition systems is clear. Firstly, we describe an existing method of optimally stopping such a process based on a modelling of the next combined result. Then, we describe approximations and assumptions which allowed us to build an optimized computation scheme and thus obtain a method with reduced computational complexity. The methods were evaluated for the tasks of document text field recognition and arbitrary text recognition in a video. The experimental comparison shows that the introduced approximations do not diminish the quality of the stopping method in terms of the achieved combined result precision, while dramatically reducing the time required to make the stopping decision. The results were consistent for both text recognition tasks.