Super-resolution reconstruction of text image with multimodal semantic interaction

Table 1. Influence of recognition accuracy on number of MSIB
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Table 1. Influence of recognition accuracy on number of MSIB
数量 CRNN准确率
easy medium hard avg
3 62.6 50.8 37.9 51.2
4 64.0 52.7 39.1 52.7
5 64.8 54.0 39.8 53.6
6 64.1 53.2 39.4 53.0
7 63.4 51.7 38.3 51.9

Table 2. Recognition accuracy of different modules
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Table 2. Recognition accuracy of different modules
Swin 语义先验 VDFI CAFM L_EA avg/%
$\times$ $\times$ $\sqrt$ $\times$ $\times$ 44.2
$\times$ $\sqrt$ $\sqrt$ $\times$ $\times$ 52.3
$\times$ $\sqrt$ $\times$ $\sqrt$ $\times$ 52.0
$\times$ $\sqrt$ $\times$ $\times$ $\sqrt$ 51.4
$\times$ $\sqrt$ $\sqrt$ $\sqrt$ $\times$ 53.2
$\sqrt$ $\sqrt$ $\times$ $\sqrt$ $\sqrt$ 52.9
$\times$ $\sqrt$ $\sqrt$ $\sqrt$ $\sqrt$ 53.6

Table 3. Impact of different fusion strategy over recognition accuracy
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Table 3. Impact of different fusion strategy over recognition accuracy
融合策略 CRNN准确率
easy medium hard avg
C 61.7 50.6 37.0 50.5
A 61.2 50.8 36.7 50.3
C+CA 61.9 51.2 37.3 50.9
CAFM 63.1 52.6 38.1 52.0

Table 4. Impact of different loss function over recognition accuracy
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Table 4. Impact of different loss function over recognition accuracy
损失 CRNN准确率
easy medium hard avg
L_GP 61.7 50.6 37.0 50.5
L_EG 62.5 51.1 37.3 51.1
L_EA 62.8 51.6 37.5 51.4

Table 5. 不同值对识别精度的影响
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Table 5. 不同值对识别精度的影响
$β$ 平均识别精度/%
$1 \times 10_{}^{- 5}$ 53.2
$1 \times 10_{}^{- 4}$ 53.6
$1 \times 10_{}^{- 3}$ 53.1
$1 \times 10_{}^{- 2}$ 52.9
$1 \times 10_{}^{- 1}$ 52.5

Table 6. Recognition accuracy of different methods on TextZoom dataset

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Table 6. Recognition accuracy of different methods on TextZoom dataset

算法	ASTER				MORAN				CRNN
算法	easy	medium	hard	avg	easy	medium	hard	avg	easy	medium	hard	avg
Bicubic	64.7	42.4	31.2	47.2	60.6	37.9	30.8	44.1	36.4	21.1	21.1	26.8
SRCNN^［3］	69.4	43.4	32.2	49.5	63.2	39.0	30.2	45.3	38.7	21.6	20.9	27.7
HAN^［4］	71.1	52.8	39.0	55.3	67.4	48.5	35.4	51.5	51.6	35.8	29.0	39.6
TSRN^［13］	75.1	56.3	40.1	58.3	70.1	53.3	37.9	54.8	52.5	38.2	31.4	41.4
PCAN^［24］	77.5	60.7	43.1	61.5	73.7	57.6	41.0	58.5	59.6	45.4	34.8	47.4
TBSRN^［25］	75.7	59.9	41.6	60.0	74.1	57.0	40.8	58.4	59.6	47.1	35.3	48.1
TG^［26］	77.9	60.2	42.4	61.3	75.8	57.8	41.4	59.4	61.2	47.6	35.5	48.9
MTSR^［27］	75.6	59.8	43.4	58.9	73.9	57.2	41.8	56.0	56.2	47.0	35.3	45.4
TATT^［16］	78.9	63.4	45.4	63.6	72.5	60.2	43.1	59.5	62.6	53.4	39.8	52.6
DPGSR^［17］	75.5	57.8	41.9	59.4	69.7	53.4	39.7	55.2	57.6	43.0	33.4	45.5
TPGSR^［14］	77.0	60.9	42.4	61.2	72.2	57.8	41.3	58.1	61.0	49.9	36.7	49.9
TPGSR-3^［14］	78.9	62.7	44.5	62.8	74.9	60.5	44.1	60.5	63.1	52.0	38.6	51.8
Ours	80.0	63.6	45.6	64.1	76.5	60.9	44.8	61.7	64.8	54.0	39.8	53.6

Table 7. PSRN and SSIM of different methods on TextZoom dataset

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Table 7. PSRN and SSIM of different methods on TextZoom dataset

算法	PSNR				SSIM
算法	easy	medium	hard	avg	easy	medium	hard	avg
Bicubic	22.35	18.98	19.39	20.35	0.788 4	0.625 4	0.659 2	0.696 1
SRCNN^［3］	23.48	19.06	19.34	20.78	0.837 9	0.632 3	0.679 1	0.722 7
HAN^［4］	23.30	19.02	20.16	20.95	0.869 1	0.653 7	0.738 7	0.759 7
TSRN^［13］	25.07	18.86	19.71	21.42	0.889 7	0.667 6	0.730 2	0.769 0
PCAN^［24］	24.57	19.14	20.26	21.49	0.883 0	0.678 1	0.747 5	0.775 2
TBSRN^［25］	23.46	19.17	19.68	20.91	0.872 9	0.645 5	0.745 2	0.760 3
TG^［26］	23.82	19.17	19.68	21.05	0.866 0	0.653 3	0.749 0	0.761 4
MTSR^［27］	23.55	19.88	19.64	21.16	0.873 4	0.684 3	0.747 6	0.773 9
TATT^［16］	24.72	19.02	20.31	21.53	0.900 6	0.691 1	0.770 3	0.793 0
DPGSR^［17］	23.36	18.76	19.77	20.77	0.871 1	0.671 9	0.750 7	0.769 8
TPGSR^［14］	23.73	18.68	20.06	20.97	0.880 5	0.673 8	0.744 0	0.771 9
TPGSR-3^［14］	24.35	18.73	19.93	21.18	0.886 0	0.678 4	0.750 7	0.777 4
Ours	24.76	19.98	20.39	21.88	0.901 3	0.697 6	0.778 0	0.797 7

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Yulan HAN, Yihong LUO, Yujie CUI, Chaofeng LAN. Super-resolution reconstruction of text image with multimodal semantic interaction[J]. Optics and Precision Engineering, 2025, 33(1): 135

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