Braille plays a vital role in education and information accessibility for visually impaired individuals. However, Braille information processing faces challenges such as data scarcity and ambiguities in mixed-text contexts. We construct English and Chinese Braille Mixed Datasets (EBMD/CBMD) with mathematical formulas to support diverse Braille domain research, and propose a syntax tree-based augmentation method tailored for Braille data. To address the underperformance of traditional fine-tuning methods in Braille-related tasks, we investigate Braille Knowledge-Based Fine-Tuning (BKFT), which reduces the learning difficulty of Braille contextual features. BrailleLLM employs BKFT via instruction tuning to achieve unified Braille translation, formula-to-Braille conversion, and mixed-text translation. Experiments demonstrate that BKFT achieves significant performance improvements over conventional fine-tuning in Braille translation scenarios. Our open-sourced datasets and methodologies establish a foundation for low-resource multilingual Braille research.

Visually impaired people are a large group who can only use braille for reading and writing. However, the lack of special educational resources is the bottleneck for educating them. Educational equity is a reflection of the level of social civilization, cultural equality, and individual dignity. Facilitating and improving lifelong learning channels for the visually impaired is of great significance. Their written braille homework or exam papers cannot be understood by sighted teachers, because of the lack of a highly accurate braille translation system, especially in Chinese which has tone marks. braille writers often omit tone marks to save space, leading to confusion when braille with the same consonants and vowels is translated into Chinese. Previous algorithms were insufficient in extracting contextual information, resulting in low accuracy of braille translations into Chinese. This project informatively fine-tuned the mT5 model with an Encoder-decoder architecture for braille to Chinese character conversion. This research created a training set of braille and corresponding Chinese text from the Leipzig Corpora. This project significantly reduced the confusion in braille, achieving $62.4$ and $62.3$ BLEU scores in the validation and test sets, with a curriculum learning fine-tuning method. By incorporating the braille recognition algorithm, this project is the first publicly available braille translation system and can benefit lots of visually impaired students and families who are preparing for the Chinese College Test and help to propel their college dreams in the future. There is a demo on our homepage\footnote{\url{https://vision-braille.com/}}.

The advancement of large language models has significantly improved natural language processing. However, challenges such as jailbreaks (prompt injections that cause an LLM to follow instructions contrary to its intended use), hallucinations (generating incorrect or misleading information), and comprehension errors remain prevalent. In this report, we present a comparative analysis of the performance of fifteen distinct models, with each model undergoing a standardized test comprising 38 queries across three key metrics: jailbreaks, hallucinations, and comprehension errors. The models are assessed based on the total occurrences of jailbreaks, hallucinations, and comprehension errors. Our work exposes these models' inherent vulnerabilities and challenges the notion of human-level language comprehension of these models. We have empirically analysed the impact of non-standard Unicode characters on LLMs and their safeguarding mechanisms on the best-performing LLMs, including GPT-4, Gemini 1.5 Pro, LlaMA-3-70B, and Claude 3 Opus. By incorporating alphanumeric symbols from Unicode outside the standard Latin block and variants of characters in other languages, we observed a reduction in the efficacy of guardrails implemented through Reinforcement Learning Human Feedback (RLHF). Consequently, these models exhibit heightened vulnerability to content policy breaches and prompt leakage. Our study also suggests a need to incorporate non-standard Unicode text in LLM training data to enhance the capabilities of these models.

Researchers have developed a robotic sensor that incorporates artificial intelligence techniques to read braille at speeds roughly double that of most human readers. The research team, from the University of Cambridge, used machine learning algorithms to teach a robotic sensor to quickly slide over lines of braille text. The robot was able to read the braille at 315 words per minute at close to 90% accuracy. Although the robot braille reader was not developed as an assistive technology, the researchers say the high sensitivity required to read braille makes it an ideal test in the development of robot hands or prosthetics with comparable sensitivity to human fingertips. The results are reported in the journal IEEE Robotics and Automation Letters.

This study aims to conduct an extensive detailed analysis of the Odia Braille reading comprehension among students with visual disability. Specifically, the study explores their reading speed and hand or finger movements. The study also aims to investigate any comprehension difficulties and reading errors they may encounter. Six students from the 9th and 10th grades, aged between 14 and 16, participated in the study. We observed participants hand movements to understand how reading errors were connected to hand movement and identify the students reading difficulties. We also evaluated the participants Odia Braille reading skills, including their reading speed (in words per minute), errors, and comprehension. The average speed of Odia Braille reader is 17.64wpm. According to the study, there was a noticeable correlation between reading speed and reading errors. As reading speed decreased, the number of reading errors tended to increase. Moreover, the study established a link between reduced Braille reading errors and improved reading comprehension. In contrast, the study found that better comprehension was associated with increased reading speed. The researchers concluded with some interesting findings about preferred Braille reading patterns. These findings have important theoretical, developmental, and methodological implications for instruction.

Text to Braille Braille is a system of raised dots that can be felt with the Conversion systems make it possible to convert written text into fingertips and used to represent letters, numbers, and symbols. It Braille, so that it can be read by people who are blind or visually was invented by Louis Braille, a French educator who was blind impaired, and they help to break down barriers to information himself, in the early 19th century as a way for people who are and education that are faced by this community. In addition, blind or visually impaired to read and write. Braille consists of Text to Braille Conversion systems can also be useful for cells of six dots arranged in two columns of three dots each.

In this paper, we have shown the improvement of English to Bharti Braille machine translation system. We have shown how we can improve a baseline NMT model by adding some linguistic knowledge to it. This was done for five language pairs where English sentences were translated into five Indian languages and then subsequently to corresponding Bharti Braille. This has been demonstrated by adding a sub-module for translating multi-word expressions. The approach shows promising results as across language pairs, we could see improvement in the quality of NMT outputs. The least improvement was observed in English-Nepali language pair with 22.08% and the most improvement was observed in the English-Hindi language pair with 23.30%.

Let's explore highlights and accomplishments of vast Google Machine Learning communities over the first quarter of the year! We are enthusiastic and grateful about all the activities that the communities across the globe do. ML Olympiad is an associated Kaggle Community Competitions hosted by Machine Learning Google Developers Experts (ML GDEs) or TensorFlow User Groups (TFUGs) sponsored by Google. The first round was hosted from January to March, suggesting solving critical problems of our time. Competition highlights include Autism Prediction Challenge, Arabic_Poems, Hausa Sentiment Analysis, Quality Education, Good Health and Well Being.

A Boston nonprofit is putting the finishing touches on the world's first affordable "tablet" for the blind, an Android-based device that is part of an innovative campaign to turn around a little-known literacy crisis among the visually impaired. "If only 12 percent of children could read today, it'd be the biggest discussion in the world," said Brian A. MacDonald, the president of National Braille Press, located in the Fenway. "But because the blind are such a small population, it's not very well known." Literacy among the blind has plummeted in the past four decades to that astonishing number -- 12 percent -- due in part to the lack of qualified Braille instructors in regular classrooms, the flipside of the mainstreaming movement. MacDonald and his team of techies hope their Braille tablet for the blind -- dubbed the B2G-20 -- will fill this void, eventually leveling the playing field for a population increasingly mired in unemployment and poverty.

According to the China Disabled Persons' Federation (CDPF), there are now 17 million visually impaired people in China, among which three million are totally blind, while the others are low-visioned. In the past two decades, China has experienced tremendous development of information technology. Traditional industries are incorporating information technology, with services delivered to users through websites and mobile applications. It is positive technical progress that visually impaired people can access various services without leaving home; for example, they can order food delivery online or schedule a taxi from an app-based transportation service. However, the development of technology has also brought challenges to the visually impaired in China.