What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

Code

• github.com/clovaai/deep-text-recognition-benchmark
  OfficialIn paperpytorch★ 0
• github.com/roatienza/deep-text-recognition-benchmark
  pytorch★ 313
• github.com/dmitrijsk/attentionhtr
  pytorch★ 64
• github.com/RobotMobile/cv-deep-learning-paper-review
  none★ 2
• github.com/luozhilin99/cnnctc
  mindspore★ 0
• github.com/2023-MindSpore-4/Code8/tree/main/cnnctc
  mindspore★ 0
• github.com/mindspore-ai/models/tree/master/official/cv/cnnctc
  mindspore★ 0
• github.com/jakartaresearch/receipt-ocr
  pytorch★ 0
• github.com/JoegameZhou/cnnctc
  mindspore★ 0
• github.com/2023-MindSpore-1/ms-code-58
  mindspore★ 0

Abstract

Many new proposals for scene text recognition (STR) models have been introduced in recent years. While each claim to have pushed the boundary of the technology, a holistic and fair comparison has been largely missing in the field due to the inconsistent choices of training and evaluation datasets. This paper addresses this difficulty with three major contributions. First, we examine the inconsistencies of training and evaluation datasets, and the performance gap results from inconsistencies. Second, we introduce a unified four-stage STR framework that most existing STR models fit into. Using this framework allows for the extensive evaluation of previously proposed STR modules and the discovery of previously unexplored module combinations. Third, we analyze the module-wise contributions to performance in terms of accuracy, speed, and memory demand, under one consistent set of training and evaluation datasets. Such analyses clean up the hindrance on the current comparisons to understand the performance gain of the existing modules.

Tasks

Benchmark Results

Reproductions