Text Detection and Recognition in Imagery: A Survey

Abstract

This paper analyzes, compares, and contrasts technical challenges, methods, and the performance of text detection and recognition research in color imagery. It summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems. Existing techniques are categorized as either stepwise or integrated and sub-problems are highlighted including text localization, verification, segmentation and recognition. Special issues associated with the enhancement of degraded text and the processing of video text, multi-oriented, perspectively distorted and multilingual 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 provides a fundamental comparison and analysis of the remaining problems in the field.

End-to-end subtitle detection and recognition for videos in East Asian languages via CNN ensemble

“…As for other text detection and recognition methods, several review papers [1,[7][8][9] can be referred to.…”

“…tection (i.e. detect texts embedded in natural static images [1]), the instinctive sequence information of videos makes it necessary to address these two tasks respectively [2]. As illustrated in Fig.1, for most videos with single-line subtitles in East Asian languages, texts at the subtitle region exhibit homogeneous properties throughout the video, including consistent STBB position, color and single character width (SCW).…”

A Novel Framework for Text Recognition in Street View Images

“…As opposed to other natural view images, street view images have quite complex contents due to the issues aforementioned. In the literature, there exist several different classification approaches for analysing text detection and recognition methodologies, which have been summarized in the following review papers [7,8]. One of those approaches categorises them into two groups, namely, stepwise and integrated methodologies.…”

“…The algorithms using the stepwise methodology primarily employ localization, validation, segmentation and recognition steps respectively. The frameworks relying on this methodology primarily follow a coarse-to-fine strategy, which first estimates position of text candidates, and then perform validation, segmentation, and recognition steps respectively [7,9,10]. Integrated methodologies, on the other hand, first utilize a character classification module, which is the most critical step and the results of this procedure are then employed by detection and recognition modules [11].…”

“…It is clear that the advent of deep learning will lead text recognition capabilities of integrated methodologies in a more advanced level [14,15]. Besides, text detection based methods can also be classified into two categories, namely, texture based approach and connected component based approaches (CC) [7,8] Text detection-based methods can be classified into two categories, namely, a texture-based approach and connected component-based approaches (CC). Texture-based approaches consider the text as a special pattern that is particularly different from the background.…”