Extraction Method of Interest Text in Image Based on Recurrent Neural Network

Fig. 10. Test results on YNIDREAL dataset. (a) Text detection results; (b) text recognition results; (c) result of interest text extraction using BLSTM-CRF model; (d) result of interest text extraction using CRF model

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Table 1. Distribution of experimental data set
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Table 1. Distribution of experimental data set
Item Dataset category Dataset type Dataset size
Train IDTRAIN Text 500
Validation IDVAL Text 100
Test YNIDREAL Image 61

Table 2. System performances of CRF and BLSTM-CRF models

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Table 2. System performances of CRF and BLSTM-CRF models

Entity	CRF			BLSTM-CRF
Entity	P /%	R /%	F₁ /%	P /%	R /%	F₁ /%
Name	75.00	68.85	71.79	86.89	86.89	86.89
Gender	96.67	95.08	95.87	96.72	96.72	96.72
Nation	95.00	93.44	94.21	93.44	93.44	93.44
Birth	90.16	90.16	90.16	91.80	91.80	91.80
Address	90.48	93.44	91.94	93.65	96.72	95.16
Idnum	92.06	95.08	93.55	90.48	93.44	91.94
Average	89.90	89.34	89.59	92.16	93.17	92.66

Table 3. Test results of integrity of interest text extraction
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Table 3. Test results of integrity of interest text extraction
Model Succeednumber Failnumber Speed /(image·s^-1) Testaccuracy /%
OCR Extraction
CRF 44 17 0.17 97 72.13
BLSTM-CRF 54 7 0.17 82 88.52

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Hengjie Yang, Zheng Yan, Zongling Wu, Dingbang Fang, Fang Duan. Extraction Method of Interest Text in Image Based on Recurrent Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(24): 241501

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