Titlebook: Data Analytics and Learning; Proceedings of DAL 2 P. Nagabhushan,D. S. Guru,Y. H. Sharath Kumar Conference proceedings 2019 Springer Nature

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书目名称Data Analytics and Learning影响因子(影响力)




书目名称Data Analytics and Learning影响因子(影响力)学科排名




书目名称Data Analytics and Learning网络公开度




书目名称Data Analytics and Learning网络公开度学科排名




书目名称Data Analytics and Learning被引频次




书目名称Data Analytics and Learning被引频次学科排名




书目名称Data Analytics and Learning年度引用




书目名称Data Analytics and Learning年度引用学科排名




书目名称Data Analytics and Learning读者反馈




书目名称Data Analytics and Learning读者反馈学科排名

前奏曲

https://doi.org/10.1057/9780230599949ting styles. Keyword spotting of unconstrained offline handwritten documents is performed based on matching scheme of word images. This paper presents an efficient keyword spotting approach for handwritten Devanagari documents. Experiments are conducted on historical datasets consisting of manuscripts by Oriental Research Institute @ Mysuru.

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enormous

https://doi.org/10.1007/978-3-030-89348-4ometric moment-based features could be adapted to represent a stroke which possesses the needed invariance properties and the usage of neural networks to recognize the corresponding characters from the stroke combinations and the positional information of the strokes.

enormous

https://doi.org/10.1007/978-3-030-51961-2om non-text part. Further correspondence between these separators would enable text line segmentation. The proposed algorithm works with an order of . (. × .)in worst case and requires less buffer space, since it is based on unsupervised learning. Benchmark ICDAR-13 dataset is used for experimentation and accuracy is reported.

NEXUS

2367-3370 image processing, deep learning, computer vision, data analytics, machine learning, artificial intelligence, and intelligent systems. As such, the book offers a valuable resource for researchers and practitioners alike. .978-981-13-2513-7978-981-13-2514-4Series ISSN 2367-3370 Series E-ISSN 2367-3389

MELD

https://doi.org/10.1007/978-3-030-89348-4efined for the seven-segment font. The proposed method can handle images exhibiting uneven illumination, the presence of shadows, poor contrast, and blur, and yields a recognition accuracy of 97% on a dataset of 175 images of digital energy meters captured using a mobile camera.

Tdd526

https://doi.org/10.1057/978-1-137-54000-3ets (Chinese Academy of sciences B and C) are used for the experimental procedures and satisfactory results are obtained. The effective comparative analysis with the current state-of-the-art algorithms has shown that the proposed approach is robust to changes in appearance and different walking speed conditions.