# Learning at Your Fingertips: An Innovative IoT-Based AI-Powered Braille Learning System
###### Abstract
The classification evaluation was carried out using DL techniques such as the GoogleNet Inception model, achieving an accuracy of 95.8%, and the sequential model, achieving a total accuracy of 92.21%. In [6], the authors proposed a touchscreen to detect Urdu braille characters using ML methods. The dataset obtained from the National Special Education School is composed of 39 classes sorted into three groups with 13 classes in each group, 144 cases for each class resulting in 5616 cases in total. The letters are input into the screen. The methodology uses a Reconstruction Independent Component Analysis (RICA)-based feature extraction model. The highest-achieving classifier was the support vector machine (SVM) with a yielded accuracy of 99.73% accuracy. However, other robust ML techniques were used such as K-nearest neighbors (KNN) and decision trees (DT) for comparison purposes. The evaluation was conducted in terms of total accuracy, true positive rate, true negative rate, false positive rate, positive predictive value, negative predictive value, and area under the receiver operating curve. Unfortunately, this study is only limited to Grade 1 Urdu braille and does not include Grade 2 Urdu braille with speech and text responses. In [7], the authors suggest using RICA-based feature extraction methods and automated tools to extract English braille alphabets. The proposed methodology uses a Grade 1 English braille dataset obtained from a touchscreen from the National Special Education School along with a position-free braille text entry technique to produce synthetic data to generate a dataset composed of 2512 cases. The dataset comprises 26 braille English letters and is divided into two classes: class 1 (1-13) and class 2 (14-26). For character recognition, Decision Trees (DT), Support Vector Machine (SVM), and K-nearest neighbor (KNN) with PCA-based feature extraction methods and Reconstruction Independent Component Analysis (RICA) were implemented. RICA outperformed PCA and the SVM classifier also achieved an accuracy of 99.85%. Sequential methods and RF methods yielded the highest accuracy with a value of 90.01%. The performance was evaluated based on total accuracy, true positive rate, true negative rate, false positive rate, positive predictive value, negative predictive value, and area under the receiver operating curve. The accuracy achieved is 100% for classes such as a, c, d, h, i, j, p, u, w, and k, 99.87 and 99.60% for other classes such as b, f, q, s, t, and v. The study is only suitable to Grade 1 English character braille and cannot be implemented with restricted computation power. The study also does not use DL methods such as CNN and GoogleNet to enhance the outcome. Authors in [8] recommend using a Histogram of Oriented Gradient Features and a Support-Vector Machine (SVM) for braille recognition and feature extraction. The method can translate Sinhala braille to Sinhala language and English braille to the English language. The images are processed, segmented, and then recognized using HOG feature extraction methods and the SVM classifier method. The study uses two types of HOG feature extraction methods: a cell size of 4 \(\times\) 4, and another one of 2 \(\times\) 2. The dataset is composed of both scanned handwritten and computer-generated braille text. The methodology can process Grade 1 English characters as well as some Grade 2 characters. The yielded accuracy was 99%. The authors report that higher processing time was needed in the case of 2 \(\times\) 2 cells compared to 4 \(\times\) 4 cells. Reference [9] advocates for using a Semantic Retrieval System to assist visually impaired individuals in mathematical studies. The methodology begins with translating a query math formula in braille into MathML code, and then the structural and semantic meaning is obtained from the MathML expression to produce a multilevel tree. The feature extraction method used is the conventional vector model.