Titlebook: Document Analysis and Recognition – ICDAR 2021; 16th International C Josep Lladós,Daniel Lopresti,Seiichi Uchida Conference proceedings 202

fodlder

RAFF

Mix-Up Augmentation for Oracle Character Recognition with Imbalanced Data Distributionamework with both the softmax loss and triplet loss on the augmented samples which proves able to improve the classification accuracy further. We conduct extensive evaluations w.r.t. both total class accuracy and average class accuracy on three benchmark datasets (i.e., Oracle-20K, Oracle-AYNU and O

顛簸下上

https://doi.org/10.1007/978-3-319-89734-9, which is out of scope for other graph-based methods in the literature. We investigate two variants of graph convolutional layers and show that learning improves performances considerably on two popular graph-based word spotting benchmarks.

Adjourn

Children in Translocal Familiesgenerating images of promising visual quality, we are able to improve classification performance by augmenting original data with generated samples. Additionally, we demonstrate that our approach is applicable to other domains as well, like digit generation in house number signs.

oxidant

單片眼鏡

Graph Convolutional Neural Networks for Learning Attribute Representations for Word Spotting, which is out of scope for other graph-based methods in the literature. We investigate two variants of graph convolutional layers and show that learning improves performances considerably on two popular graph-based word spotting benchmarks.

外形

Context Aware Generation of Cuneiform Signsgenerating images of promising visual quality, we are able to improve classification performance by augmenting original data with generated samples. Additionally, we demonstrate that our approach is applicable to other domains as well, like digit generation in house number signs.

prediabetes

Handwritten Text Recognition with Convolutional Prototype Network and Most Aligned Frame Based CTC Tors in decoding. Experiments of handwritten text recognition on four benchmark datasets of different languages show that the proposed method consistently improves the accuracy and alignment of CTC-based text recognition baseline.

originality

compose

M. Kaltenbach,G. Kober,D. Schererin time if more information is needed. Moreover our system is end-to-end trainable, OLT-C3D and the temporal reject system are jointly trained to optimize the earliness of the decision. Our approach achieves superior performances on two complementary and freely available datasets: ILGDB and MTGSetB.