What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Umapada Pal mostly deals with Artificial intelligence, Pattern recognition, Optical character recognition, Speech recognition and Natural language processing.
Umapada Pal focuses mostly in the field of Artificial intelligence, narrowing it down to matters related to Computer vision and, in some cases, Search engine indexing.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Intelligent character recognition and Intelligent word recognition.
His work deals with themes such as Character, Document processing and Character encoding, which intersect with Optical character recognition.
His Speech recognition research is multidisciplinary, relying on both Minimum bounding box and Euclidean distance.
His Natural language processing study combines topics from a wide range of disciplines, such as Field, Text detection and Devanagari.
His most cited work include:
- Indian script character recognition: a survey (479 citations)
- A complete printed Bangla OCR system (320 citations)
- An OCR system to read two Indian language scripts: Bangla and Devnagari (Hindi) (161 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, Feature extraction, Natural language processing and Speech recognition.
Umapada Pal studied Artificial intelligence and Computer vision that intersect with Cluster analysis.
His Pattern recognition research includes themes of Contextual image classification, Histogram, Image and Pixel.
The study incorporates disciplines such as Feature, Data mining, Feature, Feature vector and Image retrieval in addition to Feature extraction.
His research in the fields of Bengali overlaps with other disciplines such as Scripting language.
His Speech recognition research is multidisciplinary, incorporating elements of Intelligent word recognition, Numeral system and Pattern recognition.
He most often published in these fields:
- Artificial intelligence (84.72%)
- Pattern recognition (53.03%)
- Feature extraction (26.74%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (84.72%)
- Pattern recognition (53.03%)
- Image (10.11%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Image, Computer vision and Pixel.
His research links Natural language processing with Artificial intelligence.
His work on Bengali and Cursive as part of his general Natural language processing study is frequently connected to Scripting language, thereby bridging the divide between different branches of science.
His Feature vector study in the realm of Pattern recognition connects with subjects such as Context.
His Image research includes elements of Artificial neural network and Font.
Umapada Pal has included themes like Histogram, Sobel operator and Color space in his Pixel study.
Between 2017 and 2021, his most popular works were:
- Truncated inception net: COVID-19 outbreak screening using chest X-rays. (64 citations)
- Script identification in natural scene image and video frames using an attention based Convolutional-LSTM network (56 citations)
- ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019 (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Image and Natural language processing.
Umapada Pal integrates several fields in his works, including Artificial intelligence and Context.
His studies in Pattern recognition integrate themes in fields like Artificial neural network, Feature and Cluster analysis.
His work on Image resolution, Image signal and Structural similarity as part of general Computer vision study is frequently connected to Pipeline and Pipeline, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies deal with areas such as Time complexity and Connected component as well as Image.
His work on Cursive as part of general Natural language processing research is often related to Scripting language, thus linking different fields of science.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
