What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Basilis Gatos mainly focuses on CONTEST, Artificial intelligence, Image, Information retrieval and Context.
His research on Artificial intelligence often connects related topics like Pattern recognition.
His work is dedicated to discovering how Pattern recognition, Pixel are connected with Historical document and Precision and recall and other disciplines.
His work carried out in the field of Image brings together such families of science as Text mining and Algorithm design.
The study incorporates disciplines such as Feature extraction, Word, Image retrieval and Spotting in addition to Information retrieval.
While the research belongs to areas of Image segmentation, Basilis Gatos spends his time largely on the problem of Text segmentation, intersecting his research to questions surrounding Synthetic data and Pattern recognition.
His most cited work include:
- Adaptive degraded document image binarization (485 citations)
- ICDAR 2009 Document Image Binarization Contest (DIBCO 2009) (223 citations)
- Text line and word segmentation of handwritten documents (192 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Pattern recognition, Image segmentation, Segmentation and Image.
His biological study spans a wide range of topics, including Computer vision and Natural language processing.
The various areas that Basilis Gatos examines in his Pattern recognition study include Text mining, Pixel, Normalization and Histogram.
He interconnects Context, Hough transform and Text segmentation in the investigation of issues within Image segmentation.
His study in the field of Scale-space segmentation also crosses realms of Line and Skew.
His Image research integrates issues from Information retrieval and Pattern recognition.
He most often published in these fields:
- Artificial intelligence (79.59%)
- Pattern recognition (48.98%)
- Image segmentation (27.55%)
What were the highlights of his more recent work (between 2014-2020)?
- Artificial intelligence (79.59%)
- Pattern recognition (48.98%)
- Word (23.47%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Word, Keyword spotting and Natural language processing.
His study in Spotting, Image segmentation, Segmentation, Image and Character is done as part of Artificial intelligence.
Basilis Gatos has researched Pattern recognition in several fields, including Matching, Pixel, Computer vision and Context.
His work in Context addresses issues such as Handwriting recognition, which are connected to fields such as Information retrieval.
Basilis Gatos combines subjects such as Similarity and Hidden Markov model with his study of Word.
Basilis Gatos has included themes like Optical character recognition and Relevance feedback in his Natural language processing study.
Between 2014 and 2020, his most popular works were:
- A survey of document image word spotting techniques (93 citations)
- ICFHR2016 Handwritten Document Image Binarization Contest (H-DIBCO 2016) (71 citations)
- cBAD: ICDAR2017 Competition on Baseline Detection (50 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Information retrieval, Natural language processing and Spotting.
His Artificial intelligence research is multidisciplinary, incorporating elements of Context and Machine learning.
His Context research is multidisciplinary, relying on both Text mining, Handwriting recognition and Hidden Markov model.
Pattern recognition and Image are frequently intertwined in his study.
In general Information retrieval study, his work on Search engine indexing often relates to the realm of Field and Competition, thereby connecting several areas of interest.
His Natural language processing research incorporates themes from Character and Optical character recognition.
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