Digital Character Recognition Technique for Intelligent Vehicles in Road Scenes

Fig. 2. Comparison between proposed extraction method and traditional MSER extraction method. (a) Original diagram of a parking space number; (b) traditional MSER extraction effect; (c) MSER extraction effect for S channel

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Fig. 3. Character edge enhancement process. (a) Character candidate regions; (b) edge extraction graph of character candidate regions; (c) edge enhancement graph of characters

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Fig. 4. Edge extraction and stroke width maps of character “6”. (a) Edge extraction of character “6”; (b) stroke width of character “6”

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Fig. 5. Diagram of the Lenet-5 network

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Fig. 6. Diagrams of characters in the dataset. (a) Diagram before dataset segmentation; (b) diagram after rough dataset segmentation

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Fig. 7. Train loss and test accuracy vary with number of iterations

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Fig. 8. Experimental platform

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Fig. 9. Diagrams of character positioning results. (a) Character connected regions; (b) character location results in road scenes; (c) character connected regions; (d) character location results in road scenes

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Fig. 10. Character recognition results. (a) Recognition result of character ‘1’; (b) recognition result of character ‘4’; (c) recognition result of character ‘6’

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Table 1. Geometric constraint filter parameters
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Table 1. Geometric constraint filter parameters
Parameter Area Eccentricity Solidity Ratio
Threshold [75,600] [0.1,0.995] [0,0.4] [0.3,7]

Table 2. Experimental results of recognition rate drop-out values under different input scales
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Table 2. Experimental results of recognition rate drop-out values under different input scales
Input scale /(pixel×pixel) 18×18 24×24 28×28 36×36 48×48
Drop value /% 8.3 1.8 0.6 2.4 2.9

Table 3. Number of characters in the dataset
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Table 3. Number of characters in the dataset
Character 0 1 2 3 4 5 6 7 8 9
Number 1811 1786 1837 1826 1778 1839 1745 1805 1798 1781

Table 4. Character location performance comparison of different algorithms

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Table 4. Character location performance comparison of different algorithms

Method	C	E	T	H	R	F
Neumann^[21]	399	70	83	0.85	0.82	0.83
Epshtein^[24]	398	66	84	0.86	0.82	0.84
Lee^[7]	404	63	78	0.86	0.83	0.84
Zhang^[16]	411	54	71	0.88	0.85	0.86
Chen^[13]	417	76	65	0.84	0.86	0.85
Sung^[5]	420	60	62	0.84	0.87	0.87
Huang^[9]	422	51	60	0.89	0.89	0.88
Ours	438	50	44	0.89	0.90	0.89

Table 5. Comparison of character recognition effects of different algorithms
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Table 5. Comparison of character recognition effects of different algorithms
Method N G /%
KNN 241 80.1
HOG+SVM 237 79.4
BP Neural Network 230 76.8
Ours 265 88.6

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Rui Bai, Youchun Xu, Yongle Li, Jiong Li, Feng Xie. Digital Character Recognition Technique for Intelligent Vehicles in Road Scenes[J]. Laser & Optoelectronics Progress, 2019, 56(19): 191506

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