场景文字检测与识别：最新进展与未来趋势

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"这篇论文由Zhu Y, Yao C, Bai X共同撰写，发表在《中国计算机科学前沿》2016年第10卷第1期，页码为19-36，DOI为10.1007/s11704-015-4488-0。文章主要探讨了场景文本检测与识别的最新进展及未来趋势，重点关注在自然场景中的文本检测和识别技术，这是一个在计算机视觉和文档分析领域的重要研究课题。" 正文: 近年来，随着人工智能和计算机视觉技术的快速发展，场景文本检测与识别已经成为一个备受关注的研究领域。文本作为人类历史上最具影响力的发明之一，蕴含着丰富且精确的信息，对于各种基于视觉的应用具有极大的价值。从路牌、广告到纸质文档，文本无处不在，因此，能够在复杂自然场景中有效地检测和识别文本显得尤为重要。尽管已经取得了显著的进步，但这个领域仍面临诸多挑战，如图像噪声、模糊、扭曲、遮挡以及字体和布局的多样性。这些因素都增加了文本检测和识别的难度。本文首先回顾了最新的研究工作，对各种先进的算法进行了深入分析和比较。这些算法包括基于传统的图像处理方法，如边缘检测、连通组件分析，以及深度学习技术的应用，如卷积神经网络（CNNs）和循环神经网络（RNNs）在文本检测和识别中的创新应用。深度学习的发展极大地推动了场景文本检测与识别技术的进步。例如，使用深度学习模型可以自动学习特征表示，从而更好地处理文本的形状、结构和上下文信息。同时，端到端的训练方法使得系统能够同时进行检测和识别，提高了整体性能。此外，还有一些工作专注于解决特定问题，如密集文本检测、弯曲文本识别和多语言文本识别。文章还对未来的研究方向进行了预测。一方面，研究人员可能会更深入地探索深度学习模型的优化，例如通过引入注意力机制来提高模型对关键信息的聚焦能力。另一方面，随着计算资源的增加，大模型和大规模数据集的应用将可能进一步提升文本检测和识别的准确性和鲁棒性。此外，跨模态和跨语言的文本理解也是潜在的研究热点，这将有助于实现更智能的交互式系统和服务。该论文全面总结了场景文本检测与识别领域的现状，并对未来的趋势和发展进行了展望。随着技术的不断进步，我们期待在这个领域看到更多的创新和突破，以满足实际应用场景中日益增长的需求。

22 Front. Comput. Sci., 2016, 10(1): 19–36

classiﬁer (Fig. 4). At a later stage, the remained candidates

are grouped into text lines through a series of connection

rules. However, such connection rules can only adapt to hor-

izontal or nearly horizontal texts, therefore this algorithm is

unable to handle texts with larger inclination angle.

Fig. 3 Text detection examples of the algorithm of Epshtein et al. [9] (im-

age reprinted from Ref. [9]). This work proposed SWT, an image operator

that allows for direct extraction of character strokes from edge map

Fig. 4 Text detection examples of the algorithm of Neumann et al. [10] (im-

age reprinted from Ref. [10]). This work is the ﬁrst that introduces MSER

into the ﬁeld of scene text detection

SWT [9] and MSER [10] are two representative methods

in the ﬁeld of scene text detection, which constitute the basis

of a lot of subsequent works [12–14,29, 30,34,48,49].

The great success of sparse representation in face recogni-

tion [50] and image denoising [51] has inspired numerous re-

searchers. For example, Zhao et al. [52] constructed a sparse

dictionary from training samples and used it to judge whether

a particular area in the image contains text. However, the

generalization ability of the learned sparse dictionary is re-

stricted, so that this method is unable to handle issues like

rotation and scale change.

Diﬀerent from the aforementioned algorithms, the ap-

proach proposed by Yi et al. [28] can detect tilted texts in

natural images. Firstly, the image is divided into diﬀerent re-

gions according to the distribution of pixels in color space,

and then regions are combined into connected components

according to the properties such as color similarity, spatial

distance and relative size of regions. Finally, non-text com-

ponents are discarded by a set of rules. However, the pre-

requisite of this method is that it assumes the input images

consists of several main colors, which is not necessarily true

for complex natural images. In addition, this method relies on

a lot of artiﬁcially designed ﬁltering rules and parameters, so

that it is diﬃcult to generalize to large-scale complex image

data sets.

Shivakumara et al. [53] also proposed a method for multi-

oriented text detection. The method extracted candidate re-

gions by clustering in the Fourier-Laplace space and divided

the regions into distinct components using skeletonization.

However, these components generally do not correspond to

strokes or characters, but just text blocks. This method can

not directly compare with other methods quantitatively, since

it is not able to detect characters or words directly.

Based on SWT [9], Yao et al. [12] proposed an algorithm

that can detect texts of arbitrary orientations in natural images

(Fig. 5). This algorithm is equipped with a two-level classiﬁ-

cation scheme and two sets of rotation and rotation-invariant

features specially designed for capturing the intrinsic charac-

teristics of characters in natural scenes.

Fig. 5 Text detection examples of the algorithm of Yao et al. [12] (image

reprinted from Ref. [12]). Diﬀerent from previous methods, which have fo-

cused on horizontal or near-horizontal texts, this algorithm is able to detect

texts of varying orientations in natural images

Huang et al. [29] presented a new operator based on Stroke

Width Transform, called stroke feature transform (SFT). In

order to solve the mismatch problem of edge points in the

original Stroke Width Transform, SFT introduces color con-

sistency and constrains relations of local edge points, produc-

ing better component extraction results. The detection perfor-

mance of SFT on standard datasets is signiﬁcantly higher than

other methods, but only for horizontal texts.

In Ref. [30], Huang et al. proposed a novel framework

for scene text detection, which integrated Maximally Stable

Extremal Regions and convolutional neural networks (CNN).

The MSER operator works in the front-end to extract text

candidates, while a CNN based classiﬁer is applied to cor-

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场景文字检测与识别：最新进展与未来趋势

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