What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Bidyut B. Chaudhuri mainly focuses on Artificial intelligence, Optical character recognition, Pattern recognition, Natural language processing and Bengali.
His biological study deals with issues like Computer vision, which deal with fields such as Fractal and Fractal dimension.
His Optical character recognition research is multidisciplinary, incorporating perspectives in Speech recognition, Document processing, Devanagari, Intelligent character recognition and Character.
In his research, Algorithm, Hybrid image, Rotation and Erosion is intimately related to Image processing, which falls under the overarching field of Pattern recognition.
His Natural language processing study combines topics in areas such as Character recognition, Grammar and Identification.
His research in Bengali intersects with topics in Handwriting recognition and Character.
His most cited work include:
- An efficient differential box-counting approach to compute fractal dimension of image (600 citations)
- Texture segmentation using fractal dimension (546 citations)
- Indian script character recognition: a survey (479 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Natural language processing, Computer vision and Bengali.
Artificial intelligence is closely attributed to Speech recognition in his study.
His studies deal with areas such as Contextual image classification, Histogram and Feature as well as Pattern recognition.
His work on Natural language is typically connected to Scripting language as part of general Natural language processing study, connecting several disciplines of science.
His Bengali research is multidisciplinary, incorporating elements of Artificial neural network, Handwriting and Numeral system.
His research investigates the connection between Optical character recognition and topics such as Document processing that intersect with issues in Intelligent word recognition.
He most often published in these fields:
- Artificial intelligence (68.39%)
- Pattern recognition (33.05%)
- Natural language processing (21.84%)
What were the highlights of his more recent work (between 2015-2020)?
- Artificial intelligence (68.39%)
- Pattern recognition (33.05%)
- Natural language processing (21.84%)
In recent papers he was focusing on the following fields of study:
Bidyut B. Chaudhuri mainly investigates Artificial intelligence, Pattern recognition, Natural language processing, Bengali and Feature extraction.
The study of Artificial intelligence is intertwined with the study of Computer vision in a number of ways.
His Pattern recognition research integrates issues from Feature, Scale space, Histogram, Invariant and Zigzag.
He has included themes like Identification, Preprocessor, Support vector machine, Handwriting and Character in his Natural language processing study.
The concepts of his Bengali study are interwoven with issues in Segmentation, Feature and Born-digital.
His studies in Feature extraction integrate themes in fields like Normalization, Speech recognition and Hyperspectral imaging.
Between 2015 and 2020, his most popular works were:
- HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification (122 citations)
- Handling data irregularities in classification: Foundations, trends, and future challenges (71 citations)
- diffGrad: An Optimization Method for Convolutional Neural Networks (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of study are Artificial intelligence, Convolutional neural network, Bengali, Pattern recognition and Natural language processing.
His Artificial intelligence research includes elements of Machine learning, Speech recognition and Word recognition.
His Convolutional neural network study combines topics from a wide range of disciplines, such as Artificial neural network, Recurrent neural network, Regret, Function and Algorithm.
His biological study spans a wide range of topics, including Image restoration, Font, Computer vision and Born-digital.
His Pattern recognition research incorporates themes from Pixel, Zigzag, Deep learning and Feature.
His Natural language processing research is multidisciplinary, incorporating perspectives in Inpainting, Graph, Support vector machine and Identification.
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