title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¿Ð¾Ð»ÑÐºÐ¾Ð½ÑÑÐ¾Ð»Ð¸ÑÑÐµÐ¼Ð¾Ð³Ð¾ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ðº Ð·Ð°Ð´Ð°ÑÐµ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐ¸Ð¼Ð²Ð¾Ð»Ð¾Ð² ÐÑÐ°Ð¹Ð»Ñ Ð½Ð° ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼ÐºÐ°Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Semi-supervised learning for optical Braille recognition
Creators: name: ÐÑÐµÐ², ÐÐ°Ð»ÐµÑÐ¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð¾Ð¿ÑÐ¸ÑÐµÑÐºÐ¾Ðµ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ ÑÐ¸Ð¼Ð²Ð¾Ð»Ð¾Ð²
ÑÐ²ÑÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð¿Ð¾Ð»ÑÐºÐ¾Ð½ÑÑÐ¾Ð»Ð¸ÑÑÐµÐ¼Ð¾Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð²ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾ÑÑÐµÐ¹
ÑÑÐ¸ÑÑ Ð±ÑÐ°Ð¹Ð»Ñ
optical character recognition
convolutional neural network
semi-supervised machine learning
sequence alignment
braille

description: Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð° Ð·Ð°Ð´Ð°ÑÐ° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð¼Ð²Ð¾Ð»Ð¾Ð² ÑÑÐ¸ÑÑÐ° ÐÑÐ°Ð¹Ð»Ñ Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑÍ¾ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½ ÑÐ¿Ð¾ÑÐ¾Ð± ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ ÐºÐ°ÑÐµÑÑÐ²Ð° ÑÐ°Ð±Ð¾ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°Â­ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÑÐµÑÐ°ÑÑÐ¸Ñ ÑÑÑ Ð·Ð°Ð´Ð°ÑÑ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð½Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð±Ð¾Ð»ÑÂ­ÑÐ¸Ñ Ð¾Ð±ÑÑÐ¼Ð¾Ð² ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¹ Ð±ÐµÐ· Ð°Ð½Ð½Ð¾ÑÐ°ÑÐ¸Ð¹. ÐÐ°Ð´Ð°ÑÐ¸, ÑÐµÑÑÐ½Ð½ÑÐµ Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÑÐ¸ÑÑÐ° ÐÑÐ°Ð¹Ð»Ñ, ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐµÑÐµÐ½Ð¸Ñ Ð¸ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ. 2. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÑÐµÐ¼Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÑÐ¶Ðµ Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð³Ð¾ Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ Ñ Ð°Ð½Ð½Ð¾ÑÐ°ÑÐ¸ÑÐ¼Ð¸, Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð±ÐµÐ· Ð°Ð½Ð½Ð¾ÑÐ°ÑÐ¸Ð¹. 3. Ð¡Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ â ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¹ ÑÐµÐºÑÑÐ°, Ð½Ð°Ð¿ÐµÑÐ°ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ ÑÑÐ¸ÑÑÐ¾Ð¼ ÐÑÐ°Ð¹Ð»Ñ. 4. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð¹ ÑÑÐµÐ¼Ñ Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ðº Ð¾Ð´Ð½Ð¾Ð¼Ñ Ð¸Ð· Ð¸Ð·Ð²ÐµÑÑÐ½ÑÑ ÑÐ°Ð½ÐµÐµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¾ÑÐµÐ½ÐºÐ° ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÑÐ¾Ð¹ ÑÑÐµÐ¼Ñ. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ Ð¸ Ð´Ð¾ÑÐ°Ð±Ð°ÑÑÐ²Ð°ÐµÑÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Angelina Braille Reader, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð°Ñ Ð½Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ RetinaNet (Ð¸Ð·Ð½Ð°ÑÐ°Ð»ÑÐ½Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð½Ð° ÑÐ°Ð·Ð¼ÐµÑÐµÐ½Ð½Ð¾Ð¼ Ð½Ð°Ð±Ð¾ÑÐµ Ð¸Ð· 300 ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¹). Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð½Ð° ÑÐ·ÑÐºÐµ Python3 Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¸ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ðº, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð² ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¿ÐµÑÐ°ÑÐ½ÑÑ Ð¸Ð·Ð´Ð°Ð½Ð¸Ð¹, Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð²ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°Ð½Ð¸Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐµÐºÑÑÐ° Ð¸ Ð¸ÑÑÐ¾Ð´Ð½Ð¾Ð³Ð¾ ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½Ð¾Ð³Ð¾ ÑÐµÐºÑÑÐ° Ð¸Ð·Ð´Ð°Ð½Ð¸Ñ. ÐÐ¾Ð»ÑÑÐµÐ½ Ð½Ð°Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ (1731 ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ñ), Ðº ÐºÐ¾ÑÐ¾ÑÐ¾Ð¼Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐ½ ÑÐ¾Ð·Ð´Ð°Ð½Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼. ÐÐ°Ðº Ð¿Ð¾ÐºÐ°Ð·Ð°Ð» ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½Ñ, Ð¸ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ðº Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð´Ð¾Ð¾Ð±ÑÑÐ¸ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑ Ð½Ð° Ð½Ð¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ, ÑÐ»ÑÑÑÐ¸Ð² ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¿ÐµÑÐ°ÑÐ½Ð¾Ð³Ð¾ ÑÐµÐºÑÑÐ°. Ð¡Ð¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐ½ Ðº Ð»ÑÐ±Ð¾Ð¼Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÑÐ¸ÑÑÐ° ÐÑÐ°Ð¹Ð»Ñ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð¾Ð¼Ñ Ð½Ð° Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð¼ Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸. ÐÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸Ð¼ÑÐµ Ð¿ÑÐ¸ ÑÑÐ¾Ð¼ Ð²ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°Ð½Ð¸Ñ ÑÐµÐºÑÑÐ¾Ð² Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ Ð¿ÑÐ¾Ð²ÐµÑÑÐ¸ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¾ÑÐ¸Ð±Ð¾Ðº ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ.
description: This study explores the problem of image-based Braille recognition; a data-driven approach is proposed to improve existing machine learning algorithms that solve this problem. The aforementioned approach utilizes large amounts of unlabeled pictures with printed Braille and their source texts, aligning the original and recognized text. During the elaboration of the problem, the following tasks were completed: 1. Studying the problem of optical Braille recognition, existing approaches and data sets. 2. Development of a scheme for improving a machine learning algorithm, pre-trained on labeled data, using unlabeled data. 3. Collecting photos of text printed in Braille. 4. Application of the developed scheme along with collected data to one of the existing recognition algorithmsÍ¾ scheme evaluation. In this work, a program for optical Braille recognition named âAngelina Braille Readerâ, which is based on RetinaNet architecture and initially trained on 300 labeled pictures, is used and modified. An algorithm for correcting labels based on alignment of recognized and original text is developed and implemented. A data set of 1,731 photos has been created, to which the algorithm has been applied. As demonstrated experimentally, correcting labels allows additional training of the neural net on new data which leads to an improvement in the recognition of printed Braille. The developed technique may be applied to any optical Braille recognition algorithm based on machine learning. The alignments made in this process allow a statistical analysis of algorithm errors to be performed.