All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting

Abstract

Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision. Different from the existing approaches that formulate text detection as bounding box extraction or instance segmentation, we localize a set of points on the boundary of each text instance. With the representation of such boundary points, we establish a simple yet effective scheme for end-to-end text spotting, which can read the text of arbitrary shapes. Experiments on three challenging datasets, including ICDAR2015, TotalText and COCO-Text demonstrate that the proposed method consistently surpasses the state-of-the-art in both scene text detection and end-to-end text recognition tasks.

Toward Arbitrary-Shaped Text Spotting Based on End-to-End

“…As can be seen from Table I, our method achieved the most advanced performance in both text detection tasks and end-toend text recognition tasks. In particular, compared with the method in [28], the performance of the method in this paper is 0.9% and 2.4% higher than that of Boundary in text detection tasks and end-to-end text spotting (without lexicon) tasks respectively. Compared with [27], it improved by 3.0% in text detection tasks.…”

“…The results are shown in Table III. Compared with [28], our method showed an improvement of 1.2% and 3.7% in text detection tasks and the end-to-end text spotting with strong lexicon, respectively. In addition, compared with [27], our method improves text detection performance by 2.8%.…”

“…LabelGeneration Since the training stage requires equidistant boundary points to train TPDM, we use the algorithm in [28] to sample on the long side of the text boundary. N is also set to 7.…”

FREE: A Fast and Robust End-to-End Video Text Spotter

“…Recently, in order to sufficiently exploit the complementarity between detection and recognition, many methods [45], [4], [5], [6], [46], [7], [17], [47], [37], [48], [49], [50] are proposed to spot text in an end-to-end manner, which utilize the recognition information to optimize the localization task.…”

“…In fact, it is a very challenging task to optimize video text spotter end-to-end when taking multiple functional modules (text detection, text tracking and text recognition) into consideration, especially compared to the traditional four-staged pipeline strategy. Therefore, in this paper we develop an endto-end trainable video text spotter with only two trainable modules: the video text detector and the text recommender, similar to the end-to-end text spotting methods [6], [17], [45], [47], [48], [49] in single images.…”