Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Potanin Mark"'
Neural network structures have a critical impact on the accuracy and stability of forecasting. Neural architecture search procedures help design an optimal neural network according to some loss function, which represents a set of quality criteria. Th
Externí odkaz:
http://arxiv.org/abs/2406.12992
Predicting startup success presents a formidable challenge due to the inherently volatile landscape of the entrepreneurial ecosystem. The advent of extensive databases like Crunchbase jointly with available open data enables the application of machin
Externí odkaz:
http://arxiv.org/abs/2309.15552
Autor:
Shonenkov, Alex, Karachev, Denis, Novopoltsev, Max, Potanin, Mark, Dimitrov, Denis, Chertok, Andrey
We introduce two data augmentation techniques, which, used with a Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate (WER) and Character Error Rate (CER) beyond best-reported results on handwriting text recognition (HTR) tasks. We apply
Externí odkaz:
http://arxiv.org/abs/2112.07395
Autor:
Bakshandaeva, Daria, Dimitrov, Denis, Arkhipkin, Vladimir, Shonenkov, Alex, Potanin, Mark, Karachev, Denis, Kuznetsov, Andrey, Voronov, Anton, Davydova, Vera, Tutubalina, Elena, Petiushko, Aleksandr
Supporting the current trend in the AI community, we present the AI Journey 2021 Challenge called Fusion Brain, the first competition which is targeted to make the universal architecture which could process different modalities (in this case, images,
Externí odkaz:
http://arxiv.org/abs/2111.10974
This paper proposes a handwritten text recognition(HTR) system that outperforms current state-of-the-artmethods. The comparison was carried out on three of themost frequently used in HTR task datasets, namely Ben-tham, IAM, and Saint Gall. In additio
Externí odkaz:
http://arxiv.org/abs/2108.11667
Autor:
Potanin, Mark, Dimitrov, Denis, Shonenkov, Alex, Bataev, Vladimir, Karachev, Denis, Novopoltsev, Maxim
This paper presents a new dataset of Peter the Great's manuscripts and describes a segmentation procedure that converts initial images of documents into the lines. The new dataset may be useful for researchers to train handwriting text recognition mo
Externí odkaz:
http://arxiv.org/abs/2103.09354
Autor:
Karachev Denis, Chertok Andrey, Novopoltsev Maxim, Potanin Mark, Dimitrov Denis, Shonenkov Alex, Bataev Vladimir
Publikováno v:
HIP@ICDAR
This paper presents a new dataset of Peter the Great’s manuscripts and describes a segmentation procedure that converts initial images of documents into lines. This new dataset may be useful for researchers to train handwriting text recognition mod