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Found 8 papers, 4 papers with code
Abugida Normalizer and Parser for Unicode texts
This paper proposes two libraries to address common and uncommon issues with Unicode-based writing schemes for Indic languages.
BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset
While strides have been made in deep learning based Bengali Optical Character Recognition (OCR) in the past decade, the absence of large Document Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e. g., transcribing historical documents and newspapers.
TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla
Many areas, such as the biological and healthcare domain, artistic works, and organization names, have nested, overlapping, discontinuous entity mentions that may even be syntactically or semantically ambiguous in practice.
A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes
We propose a labeling scheme based on graphemes (linguistic segments of word formation) that makes segmentation in-side alpha-syllabary words linear and present the first dataset of Bengali handwritten graphemes that are commonly used in an everyday context.
SegCodeNet: Color-Coded Segmentation Masks for Activity Detection from Wearable Cameras
In comparison to a single-stream network, the proposed two-stream method achieves an absolute improvement of $14. 366\%$ and $10. 324\%$ for averaged F1 score and accuracy, respectively, when average results are compared for three different frame sizes $224\times224$, $112\times112$, and $64\times64$.
Privacy-Aware Activity Classification from First Person Office Videos
In the advent of wearable body-cameras, human activity classification from First-Person Videos (FPV) has become a topic of increasing importance for various applications, including in life-logging, law-enforcement, sports, workplace, and healthcare.
X-Ray Image Compression Using Convolutional Recurrent Neural Networks
To the best of our knowledge, this is the first reported evaluation on using a deep convolutional RNN for medical image compression.
End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets
The experimental results demonstrate the superiority of the proposed network compared to the best existing method, providing a relative improvement in epoch-wise average accuracy of 6. 8% and 6. 3% on the household data and multi-source data, respectively.
