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@article{196414,
        author = {J Krishna Manasa and Nukala Manasa and Basireddy Shivani and Dr Potu Narayana},
        title = {Context-Aware Label Information Extraction Using Vision + NLP for Assistive Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2580-2588},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196414},
        abstract = {Product labels contain essential information about the brand name, product name, and quantity, which can serve a wide variety of applications as assistive and automated systems. However, a problem arises when attempting to extract this type of information from a product image due to the different font styles, varying light exposure, and background noise found in these types of images. This paper proposes a multi-OCR-based approach for extracting label information from a product image. The system is based upon the use of multiple OCR engines (Tesseract, EasyOCR and PaddleOCR) in order to detect and identify text in product labels. Once the text has been extracted, additional preprocessing and fuzzy matching with RapidFuzz techniques will improve the extraction of label information. The proposed system was evaluated using several performance measurements (accuracy, precision, recall, F1 score and error rate). The experimental results indicate that the proposed approach achieved 76.92% accuracy, 100% precision, 76.92% recall, and an F1 score of 86.96% in extracting product label information from images.},
        keywords = {Optical Character Recognition (OCR), Product Label Recognition, Multi-OCR System, Text Extraction, Image Processing, Information Extraction, Deep Learning, Assistive Applications.},
        month = {April},
        }