International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Energy IMG to XL Sheet Generator |
|---|---|
| ABSTRACT | The IMG TO XL SHEET GENERATOR project is an automated system designed to convert image-based tabular data directly into structured Excel spreadsheets without using Optical Character Recognition (OCR). Instead of extracting text character-by-character, the system focuses on detecting table structures, cell boundaries, and visual patterns within images to reconstruct the data layout in spreadsheet format. The solution utilizes image processing and computer vision techniques to identify rows, columns, grid lines, and segmented regions. By analyzing pixel patterns, contours, and geometric alignments, the system maps detected table cells into corresponding Excel cells. This approach is particularly effective for images containing clearly formatted tables, charts, or structured datasets where layout recognition is more important than textual interpretation. |
| AUTHOR | N. VISWANADHA REDDY, U. YUVA SHASHANK, T. ANIL, B. PRANEETH KUMAR, S. MANASA, P. NARASIMHA, K. MAHESH BABU Senior Assistant Professor, Department of CSE (AI&ML), NSRIT, Visakhapatnam, India U.G. Student, Department of Computer Engineering (AI&ML), NSRIT, Visakhapatnam, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403105 |
| pdf/105_Energy IMG to XL Sheet Generator.pdf | |
| KEYWORDS | |
| References | [1] Smith, R. “An Overview of the Tesseract OCR Engine.” Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), IEEE, 2007. [2] Kieninger, T., & Dengel, A. “Applying the T-Recs Table Recognition System to the Business Letter Domain.” International Journal on Document Analysis and Recognition (IJDAR), Vol. 2, No. 2–3, 1999. [3] Shigarov, A., Mikhailov, A., & Altaev, A. “Configurable Table Structure Recognition in Scanned Documents.” Expert Systems with Applications, Vol. 42, Issue 23, 2015. [4] Bradski, G. “The OpenCV Library.” Dr. Dobb’s Journal of Software Tools, 2000. [5] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. “You Only Look Once: Unified, Real-Time Object Detection.” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. [6] Garain, U., & Chaudhuri, B. B. “Recognition of Handwritten/Printed Mixed Form Numerals in Indian Scripts.” Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2005. [7] Zhong, X., Tang, J., & Yepes, A. J. “PubTabNet: Structured Table Recognition Using Image-Based Deep Learning.” Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2019. [8] Gilani, A., Qasim, S. R., Malik, I., & Shafait, F. “Table Detection Using Deep Learning.” Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2017. [9] Siddiqui, S. A., Fatehpuria, P., Dengel, A., & Ahmed, S. “Deeptabstr: Deep Learning Based Table Structure Recognition.” Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2019. [10] Antonacopoulos, A., Bridson, D., Papadopoulos, C., & Pletschacher, S. “A Realistic Dataset for Performance Evaluation of Document Layout Analysis.” Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2009. |
