Image-Based Recognition of Braille Using Neural Networks on Mobile Devices

Baumgärtner, Christopher; Schwarz, Thorsten; Stiefelhagen, Rainer

doi:10.1007/978-3-030-58796-3_41

Cited by 1 publication

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results highlighted in boldface font in Tab. 5 show that the DCNN model improves the performance of OBR and outperforms the model proposed in recent work. Limitations remain for our work even though the F1-scores of the proposed model are 99.30% and 99.00% for recognizing Braille cells of dataset 1 and dataset 2, respectively.…”

mentioning

confidence: 66%

“…Recently, deep learning-based OBR models have been proposed for developing OBR systems and have achieved signi cant performance, including the models used by Hsu [4], Li et al [20], Baumgärtner et al [5], Kawabe et al [21], and Shokat et al [22]. In addition, Shokat et al [23] presented a comprehensive survey analysis of OBR methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A total of 74 images are not enough for deep learning, and 30 images may not give a fair evaluation of the robustness of the model. Baumgärtner et al [5] used a dataset of German Braille symbols to propose a mobile OBR based on a deep learning classi cation. They used Faster R-CNN and both CNN and long short-term memory (LSTM) for line detection and model classi cation, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Assistive technology would also be helpful to convert Braille symbols into texts or voices to support students in regular schools or universities. Sighted people may also face dif culty understanding and reading Braille symbols that visually impaired people print as Braille scripts in different sciences such as math or chemistry [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Deep Learning-Based Recognition Approach for the Conversion of Multilingual Braille Images

Al-Salman¹,

Gumaei²,

AlSalman³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Braille-assistive technologies have helped blind people to write, read, learn, and communicate with sighted individuals for many years. These technologies enable blind people to engage with society and help break down communication barriers in their lives. The Optical Braille Recognition (OBR) system is one example of these technologies. It plays an important role in facilitating communication between sighted and blind people and assists sighted individuals in the reading and understanding of the documents of Braille cells. However, a clear gap exists in current OBR systems regarding asymmetric multilingual conversion of Braille documents. Few systems allow sighted people to read and understand Braille documents for self-learning applications. In this study, we propose a deep learning-based approach to convert Braille images into multilingual texts. This is achieved through a set of effective steps that start with image acquisition and preprocessing and end with a Braille multilingual mapping step. We develop a deep convolutional neural network (DCNN) model that takes its inputs from the second step of the approach for recognizing Braille cells. Several experiments are conducted on two datasets of Braille images to evaluate the performance of the DCNN model. The rst dataset contains 1,404 labeled images of 27 Braille symbols representing the alphabet characters. The second dataset consists of 5,420 labeled images of 37 Braille symbols that represent alphabet characters, numbers, and punctuation. The proposed model achieved a classi cation accuracy of 99.28% on the test set of the rst dataset and 98.99% on the test set of the second dataset. These results con rm the applicability of the DCNN model used in our proposed approach for multilingual Braille conversion in communicating with sighted people.

show abstract

mentioning

confidence: 66%