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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書目名稱Data Analytics and Learning影響因子(影響力)




書目名稱Data Analytics and Learning影響因子(影響力)學(xué)科排名




書目名稱Data Analytics and Learning網(wǎng)絡(luò)公開度




書目名稱Data Analytics and Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Analytics and Learning被引頻次




書目名稱Data Analytics and Learning被引頻次學(xué)科排名




書目名稱Data Analytics and Learning年度引用




書目名稱Data Analytics and Learning年度引用學(xué)科排名




書目名稱Data Analytics and Learning讀者反饋




書目名稱Data Analytics and Learning讀者反饋學(xué)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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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.