Text Detection and Recognition in images: A survey

TL;DR

This survey comprehensively reviews methods for text detection and recognition in images, covering fundamental challenges, techniques, special issues, datasets, and performance comparisons to guide future research.

Contribution

It categorizes existing techniques, highlights sub-problems, and provides a detailed comparison of approaches and datasets in the field of image text detection and recognition.

Findings

Existing methods are categorized as stepwise or integrated.

Special issues like degraded text and multi-oriented text are addressed.

Benchmark datasets and performance comparisons are provided.

Abstract

Text Detection and recognition is a one of the important aspect of image processing. This paper analyzes and compares the methods to handle this task. It summarizes the fundamental problems and enumerates factors that need consideration when addressing these problems. Existing techniques are categorized as either stepwise or integrated and sub-problems are highlighted including digit localization, verification, segmentation and recognition. Special issues associated with the enhancement of degraded text and the processing of video text and multi-oriented text are also addressed. The categories and sub-categories of text are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared. This review also provides a fundamental comparison and analysis of the remaining problems in the field.