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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Dayananda Kodala Jayaram
ATME College of Engineering affiliated to Visvesvaraya Technological University
Computer Science & IT Engineering

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E-TEXTLOC: efficient text localization and extraction in real-world video scenes Dayananda Kodala Jayaram; Puttegowda Devegowda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3537-3545

Abstract

Localization of an accurate text is quite a complicated issue, especially when attempting to extract from a complex video. It is mainly due to the limitation of resources, dynamic background, and text variability. There is various artificial intelligence based methods adopting machine learning for addressing such issues encounter lower positional accuracy and incur maximized computational cost. Therefore, the proposed system introduces efficient text localization and extraction for comprehensive positional accuracy in complex videos (E-TEXTLOC). Different from conventional approaches, E-TEXTLOC facilitates sampling of video frames while MobileNetV2 is deployed towards faster localization of text. The outcome of recognized text is further refined by a verifier module, which provides a self-supervised response. Assessed on the YouTube video dataset, the proposed model accomplishes 98.2% accuracy with 29.6 ms towards generating analytical outcomes. It means the proposed model accomplishes 6-10% accuracy enhancement with a 40-50% reduction of speed in contrast to the existing system. The implications of the proposed study can be stated towards surveillance system, autonomous vehicles, and assistive devices that works in real-time.
A deep learning framework for robust text extraction from heterogeneous image sources Dayananda Kodala Jayaram; Puttegowda Devegowda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4724-4732

Abstract

Text extraction from an image is characterized by various challenges that result in various evolving solutions at current times. Existing solutions using artificial intelligence (AI) do have some promising outcomes, but also have their own shortcomings associated with accuracy and efficiency. The manuscript introduces a simplified and yet innovative AI framework towards extracting text by unifying character region-aware text detection. The model uses a convolutional neural network (CNN) along with transformer-centric optical character recognition (OCR) towards better performance on scene text recognition. Analysis is carried out on the standard and benchmarked International Conference on Document Analysis and Recognition 2015 (ICDAR 2015) dataset to find that the proposed model accomplished 94.7% accuracy and 0.82 seconds per image of response time, which are much significant improvements in performance, while observed in contrast to existing AI models, proving the model to deliver high-performance and cost-effective outcomes.
Co-Authors Puttegowda Devegowda