SANIP: Shopping Assistant and Navigation for the visually impaired
This addresses shopping accessibility for blind individuals, though it appears incremental as it combines existing detection methods.
The researchers developed SANIP, a shopping assistant system for visually impaired people that detects handheld objects, text, and barcodes, converting information to speech. The system successfully detected trained objects like Parle-G, Tide, and Lays with good accuracy and precision.
The proposed shopping assistant model SANIP is going to help blind persons to detect hand held objects and also to get a video feedback of the information retrieved from the detected and recognized objects. The proposed model consists of three python models i.e. Custom Object Detection, Text Detection and Barcode detection. For object detection of the hand held object, we have created our own custom dataset that comprises daily goods such as Parle-G, Tide, and Lays. Other than that we have also collected images of Cart and Exit signs as it is essential for any person to use a cart and also notice the exit sign in case of emergency. For the other 2 models proposed the text and barcode information retrieved is converted from text to speech and relayed to the Blind person. The model was used to detect objects that were trained on and was successful in detecting and recognizing the desired output with a good accuracy and precision.