Optical Character Recognition + Instance Segmentation for russian and english languages

Related tags

Deep Learningocrtorchsegmentationcopypasteocr-recognitioninstance-segmentationkenlmbeam-search-decoderdetectron2
Overview

Распознавание рукописного текста в школьных тетрадях

Соревнование, проводимое в рамках олимпиады НТО, разработанное Сбером. Платформа ODS.

Результаты Public

leaderbord

Задача

Вам нужно разработать алгоритм, который способен распознать рукописный текст в школьных тетрадях. В качестве входных данных вам будут предоставлены фотографии целых листов. Предсказание модели — список распознанных строк с координатами полигонов и получившимся текстом.

Как должно работать решение?

Последовательность двух моделей: сегментации и распознавания. Сначала сегментационная модель предсказывает полигоны маски каждого слова на фото. Затем эти слова вырезаются из изображения по контуру маски (получаются кропы на каждое слово) и подаются в модель распознавания. В итоге получается список распознанных слов с их координатами.

Модели

Instance Segmentation Open In Colab

  • модель X101-FPN из зоопарка моделей detectron2 + аугментации + высокое разрешение

Optical Character Recognition (OCR) Open In Colab

  • архитектура CRNN с бекбоном Resnet-34, предобученным на топ 1 модели соревнования Digital Peter

Beam Search Open In Colab

  • модель KenLM, обученная на данных сорвенования Feedback, Решу ОГЭ/ЕГЭ, а также CTCDecoder

Ресурсы & Submit

Christofari с NVIDIA Tesla V100 и образом jupyter-cuda10.1-tf2.3.0-pt1.6.0-gpu:0.0.82

Мы не гарантируем поддержку сабмита всё время, поэтому предоставляем 2 ссылки: Google Drive и Yandex

Цитирование

@misc{nto-ai-text-recognition,
  author =       {Arseniy Shahmatov and Gerasomiv Maxim},
  title =        {notebook-recognition},
  howpublished = {\url{https://github.com/Lednik7/nto-ai-text-recognition}},
  year =         {2022}
}
Releases(v1.0.0)
Owner
Gerasimov Maxim
Data Scientist. Graduated by Yandex Lyceum and Tinkoff Education
Gerasimov Maxim
GitHub Repository https://t.me/gradientdip
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