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Scene text detection and recognition: a survey

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Abstract

Scene text detection and recognition have been given a lot of attention in recent years and have been used in many vision-based applications. In this field, there are various types of challenges, including images with wavy text, images with text rotation and orientation, changing the scale and variety of text fonts, noisy images, wild background images, which make the detection and recognition of text from the image more complex and difficult. In this article, we first presented a comprehensive review of recent advances in text detection and recognition and described the advantages and disadvantages. The common datasets were introduced. Then, the recent methods compared together and analyzed the text detection and recognition systems. According to the recent decade studies, one of the most important challenges is curved and vertical text detection in this field. We have expressed approaches for the development of the detection and recognition system. Also, we have described the methods that are robust in the detection and recognition of curved and vertical texts. Finally, we have presented some approaches to develop text detection and recognition systems as the future work.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran

    Fatemeh Naiemi & Vahid Ghods

  2. Department of Electrical and Computer Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran

    Hassan Khalesi

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  1. Fatemeh Naiemi
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  2. Vahid Ghods
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Appendix (Abbreviations)

Appendix (Abbreviations)

AN :

attention network

AON :

arbitrarily-oriented text recognition

CA-FCN :

character attention fully convolutional network

CCA :

connected component analysis

CNN :

convolutional neural network

CRF :

conditional random field

CRAFT :

character region awareness for text detection

CRNN :

combination of CNN with RNN

CTPN :

connectionist text proposal network

DR :

direct regressor

end-to-end recognition :

text detection and recognition system

EAST :

efficient and accurate scene text detector

ECT :

edge color transform

ESIR :

end-to-end trainable scene text recognition network by iterative rectification

ER :

extremal region

FAN :

focusing attention network

FCN :

fully convolutional networks

FN :

focusing network

FTPN :

feature pyramid-based text proposal network

FTSN :

fused text segmentation networks

GRCNN :

gated recurrent convolution neural network

GVF :

gradient vector flow

i.inception :

improved inception layer

i.ReLU :

improved ReLU layer

IRM :

iterative refinement module

IIIT5K :

IIIT 5 K-Words

LOMO :

look more than once

LWDP :

local word directional pattern

MIL :

multiple instance learning

MLP :

multi-layer perceptron

MOSTL :

an accurate multi oriented scene text localization

MSER :

maximally stable extremal region

MSP-Net :

multiscale spatial partition network

new.i.inception layers :

new improved inception layer

new.i.ReLU :

new improved ReLU layer

NMS :

non-maximum suppression

PLN :

part localization network

P-CNN :

part-based convolutional neural network

PSENet :

progressive scale expansion network

R2AM :

recursive recurrent neural networks with attention modeling

RARE :

robust text recognizer with automatic rectification

RICNN :

rotation-invariant CNN

RPN :

region proposal network

RRPN :

rotation region proposal networks

RRD :

rotation-sensitive regression for oriented scene text detection

SE :

squeeze-and-excitation

SEM :

shape expression module

SRN :

sequence recognition network

SPL :

self-paced learning

STRHOG :

scale and translation robust HOG

STN :

spatial transformer network

SVT :

street view text

SWT :

Stroke width transform

wDTW :

weighted dynamic time warping

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Naiemi, F., Ghods, V. & Khalesi, H. Scene text detection and recognition: a survey. Multimed Tools Appl 81, 20255–20290 (2022). https://doi.org/10.1007/s11042-022-12693-7

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