Discrimination of Handwritten and Printed Texts Based on Frame Features and Viterbi Decoder

Fig. 5. Discrimination results of handwritten and printed texts. (a) Frame feature decoding results mapped to text line images; (b) longitudinal image segmentation; (c) re-determination results in each region

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Table 1. Convolutional neural network structure of OCR based on text line

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Table 1. Convolutional neural network structure of OCR based on text line

Layer name	Output size	Convolution kernel
conv1-1conv1-2conv1-3pool1	24×[(W-3)/2+1]	32@3×3, d_pad=164@1×164@3×3, d_pad=13×3 Max pool, e_stride=2
conv2-1conv2-2conv2-3conv2-4pool2	6×[(W-3)/2-3]	64@1×1128@3×3, e_{stride_h}=264@1×1128@3×3, d_pad=13×3 Max pool, e_{stride_h}=2
conv3-1conv3-2fc	1×[(W-3)/2-3]	256@3×1, d_pad=1128@3×1S@1×1

Table 2. Experimental test results based on frame features and Viterbi decoder%
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Table 2. Experimental test results based on frame features and Viterbi decoder%
Method HandwrittenAccuracy PrintedAccuracy
HOG+SVM 67.24 61.55
GMM+Viterbi 72.90 88.65

Table 3. Experimental results based on frame features and Viterbi decoding followed by post-processing
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Table 3. Experimental results based on frame features and Viterbi decoding followed by post-processing
Method Handwrittenaccuracy /% Printedaccuracy /% Frame /s
GMM+Viterbi 72.90 88.65 502
GMM+Viterbi+post-processing 78.04 89.12 496
BiLSTM 79.28 89.91 39

Table 4. Character recognition accuracy of different discrimination methods of handwritten and printed texts%
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Table 4. Character recognition accuracy of different discrimination methods of handwritten and printed texts%
Method Handwrittenaccuracy Printedaccuracy
Sent Word Sent Word
Artificialsegmentation 64.92 73.01 84.67 92.10
GMM+Viterbi+post-processing 61.02 69.18 82.31 90.56
HOG+SVM 57.85 66.43 79.62 87.95

Table 5. Classification accuracy of handwritten and printed texts after post-processing in each scene%

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Table 5. Classification accuracy of handwritten and printed texts after post-processing in each scene%

Scene	HOG+SVM		GMM+Viterbi+post-processing
Scene	Handwritten	Printed	Handwritten	Printed
Signed document	67.24	61.55	78.04	89.12
Natural scene	63.81	57.49	76.32	86.71
Table	65.29	57.43	72.66	86.36
Noisy document	60.31	55.23	71.48	82.23

Table 6. Character recognition accuracy of handwritten and printed texts in different scenes%

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Table 6. Character recognition accuracy of handwritten and printed texts in different scenes%

Scene	HOG+SVM				GMM+Viterbi+post-processing
	Handwritten		Printed		Handwritten		Printed
	Sent	Word	Sent	Word	Sent	Word	Sent	Word
Signed document	57.85	66.43	79.62	87.95	61.02	69.18	82.31	90.56
Natural scene	53.05	60.92	72.29	78.72	55.59	64.96	78.44	82.86
Table	54.61	61.98	73.89	78.73	55.16	65.01	78.60	85.21
Noisy document	45.35	54.87	66.40	72.56	48.21	56.52	68.73	76.67

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Qin Lin, Junfeng Xia, Zhengzheng Tu, Yutang Guo. Discrimination of Handwritten and Printed Texts Based on Frame Features and Viterbi Decoder[J]. Laser & Optoelectronics Progress, 2019, 56(6): 061003

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Paper Information

Category: Image Processing

Received: Aug. 21, 2018

Accepted: Oct. 10, 2018

Published Online: Jul. 30, 2019

The Author Email: Lin Qin (linqin@hfnu.edu.cn)

DOI:10.3788/LOP56.061003

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Convolutional neural network structure of OCR based on text line

Table 1. Convolutional neural network structure of OCR based on text line

Table 2. Experimental test results based on frame features and Viterbi decoder%

Table 2. Experimental test results based on frame features and Viterbi decoder%

Table 3. Experimental results based on frame features and Viterbi decoding followed by post-processing

Table 3. Experimental results based on frame features and Viterbi decoding followed by post-processing

Table 4. Character recognition accuracy of different discrimination methods of handwritten and printed texts%

Table 4. Character recognition accuracy of different discrimination methods of handwritten and printed texts%

Table 5. Classification accuracy of handwritten and printed texts after post-processing in each scene%

Table 5. Classification accuracy of handwritten and printed texts after post-processing in each scene%

Table 6. Character recognition accuracy of handwritten and printed texts in different scenes%

Table 6. Character recognition accuracy of handwritten and printed texts in different scenes%