Students find it difficult to learn the syntax of programming languages when they are new to coding. Some students get discouraged and even quit their coding journey because of having trouble with memorizing all the syntactic rules of a programming language. Flowcharts are an effective way to help students visualize the logic of an algorithm developed to solve a problem. But some students find it challenging to convert their flowcharts into executable code and even when they do, some get frustrated because of the errors they encounter while running the code they have written. So students who are not well versed with the syntax of a programming language can face difficulty debugging their programs even when they understand the logic behind the algorithms. This research aimed at developing a tool that can assist young students with less coding experience to learn programming. An intuitive user interface for the application was developed using a python library called gradio. The functionality of taking flowchart images as user inputs and providing equivalent python code with a brief explanation was incorporated on the UI. Text from the uploaded flowchart image is extracted in 3 steps which include feature extraction using deep learning models like ResNet and VGG, sequence labelling using Long Short-Term Memory networks and decoding the sequence labels using Connectionist Temporal Classification algorithms. The extracted text is then provided to an open source large language model with 70 billion parameters called Llama-2-Chat for the generation of the python code. The generated python code is displayed on the gradio UI along with a brief explanation of the code. The user can copy the code and run it on the IDE of their choice. The application was evaluated with a test dataset of several flowchart images. The generated python codes for the test flowchart images were executed in a code editor and the produced outputs were found to be correct. The generated code got executed without any errors for 75 % of the flowchart images from the test dataset.

This paper explores sign language, a natural mode of communication for the deaf community. However, sign language often remains challenging to learn and creates communication barriers between the deaf and the hearing. This work addresses this issue by assessing the performance of the state-of-the-art convolutional model, ConvNeXt, on the novel task of sign language recognition. The research yields compelling results, with accuracies surpassing 99% and fast training times that rival advanced Vision Transformers (ViTs). The experiments are rigorously evaluated using the publicly available Sign-Language-MNIST Dataset, an established benchmark for sign language research. A comparison of the generalizability of ConvNeXt and ViT is further undertaken using the publicly available Indian Sign Language Dataset which shows ViTs generalize better by ~3% in sign language recognition tasks. The findings of this study contribute to the broader goal of improving communication for the deaf community while also highlighting the capability of carefully constructed lightweight convolutional models that have recently fallen out of favour.