Recognition of Printed Kannada Text in Scene Images using Machine Learning Techniques
Keywords:
Filters, Kannada text, Text Recognition, LSTM Model, YOLO V7Abstract
Images are essential and plays a key and an important function in an electronics and communication media for sharing the information. In recent days, every activity has to be recorded as a digital image. Despite the rapid progress in the growth of strong deep learning representations for detecting and recognizing scene text, current state-of-the-art techniques fall short of delivering satisfactory outcomes in complex scenarios, such as those involving diverse logos, decorated boards, or text embedded within components. This proposed research article provides a new approach to enhancing the detection of scene text and recognition of scene text performances by detecting defects in the detection results. In this research, four important stages such as pre-processing, Kannada text detection, feature extraction and recognition are carried after the collection of multilingual scene text images. Initially, the multilingual scene text images are collected and is preprocessed for the effectual removal of noise using bilateral filter and wiener filter for enrich the image quality. After pre-processing, detection step is performed for analyzing only Kannada text by leaving the other texts using Convolutional block attention-based YOLO V7. The highly discriminative feature/s are picked out from the detected scene images by using Dense stacked LSTM network model. Finally, the Convolutional Residual network assisted auto encoder model is employed for optimal recognition of Kannada language can be created using machine-readable strings. This yields the better recognition rate compared to cutting-edge techniques.
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