2019
DOI: 10.1109/access.2019.2916616
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Natural Scene Text Recognition Based on Encoder-Decoder Framework

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Cited by 56 publications
(20 citation statements)
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“…The overall classification accuracy in the article is obtained by calculating the confusion matrix from the verification sample. Many studies have demonstrated that adding texture features can improve classification accuracy [61,62]. The green line refers to classification accuracy with additional parameters such as Normalized Difference Water Index (NDWI), Radio Vegetation Index (RVI), NDVI, Enhanced Vegetation Index (EVI), and Normalized Difference Building Index (NDBI), the red line refers to classification accuracy after selection using texture features, optimal windows, climatic factors, and feature parameters ( Figure 6).…”
Section: (1) Analysis Of 33-years Preliminary Classification Resultsmentioning
confidence: 99%
“…The overall classification accuracy in the article is obtained by calculating the confusion matrix from the verification sample. Many studies have demonstrated that adding texture features can improve classification accuracy [61,62]. The green line refers to classification accuracy with additional parameters such as Normalized Difference Water Index (NDWI), Radio Vegetation Index (RVI), NDVI, Enhanced Vegetation Index (EVI), and Normalized Difference Building Index (NDBI), the red line refers to classification accuracy after selection using texture features, optimal windows, climatic factors, and feature parameters ( Figure 6).…”
Section: (1) Analysis Of 33-years Preliminary Classification Resultsmentioning
confidence: 99%
“…Emerging memory-efficient deep neural network architectures are capable of storing contextual information and process the sequence of features more efficiently. In [5], CNN based encoder/decoder architecture is proposed to extract and recognize the ordered feature sequence. Text is recognized by the Bidirectional long short-term memory (Bi-LSTM) network.…”
Section: Review Of Scene Text Recognition Methodsmentioning
confidence: 99%
“…The technique is simple and cost-effective. The variants of RNN such as LSTM and BLSTM [5,6,7,10] are memory efficient and capable to store contextual information for a longer duration. In any word recognition problem, the contextual information of the previous and next character is equally important and hence BLSTM architectures are gaining more popularity.…”
Section: Review Of Scene Text Recognition Methodsmentioning
confidence: 99%
“…Causes artificial effects such as sawtooth, ringing interference; The reconstruction-based method [9] is based on a specific degradation model to provide constraints on high-resolution image reconstruction based on the observed low-resolution image sequence, and then fuses different information of the same scene to obtain high quality. The reconstruction results can better suppress the artificial effects, but also cause the loss of detailed information, and the method is complicated in operation, difficult to guarantee accuracy, and low in efficiency; With the optimization of processor performance, convenient conditions have been provided for the field of big data and artificial intelligence, and deep learning applications have become more widespread [10], [11]. Learning-based algorithms are currently hotspots in the field of super-resolution [12], the algorithm learns the mapping relationship between the high-resolution image and the low-resolution image by extracting features, and finally realizes image reconstruction.…”
Section: Introductionmentioning
confidence: 99%