Abstract
Text understanding from natural scene images has progressively gained much interest in computer vision due to the frequent emergence of handheld or wearable devices. Sometimes, these devices capture low-quality images. As a result, the image can be disrupted with unpredictable blur caused by text movement and camera shake. Further, the problem is integrated with edge intelligence, an emerging paradigm that drives the computing services, applications, and tasks from mainstream cloud to network edge. The purpose is to minimize the bandwidth cost and latency and improves privacy. Hence, this research introduces a novel semi pipelined technique to address such challenging issue for edge intelligence. The fundamental contributions of this work are as given: (i) after text image enhancement, the natural text images are blurred to create a synthetic dataset; (ii) we introduced image-based 2D Radix-4 DIT FFT and the inverse to deblur the blurred images; (iii) after that, text understanding process is applied on the recovered images. Firstly, the text localization and segmentation are taken out using a novel open contour and 4connected edge-based region approach. Secondly, the recovered images are classified into text and non-text classes adopting multimodal feature representation. Thirdly, Character Labeling Convolution Neural Network (CL-CNN) model is introduced for character labeling by extracting deep features to work fine on discriminative and ambiguous text. Finally, the experiments validated that the proposed framework achieved promising results on ICDAR 2003, SVT, and IIIT5K compared with standard techniques substantially and from blur images efficiently and flexibly.