Enhancement in web accessibility for visually impaired people using hybrid deep belief network –bald eagle search

Tiwary, Tejal; Mahapatra, Rajendra Prasad

doi:10.1007/s11042-023-14494-y

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…As shown in Table 1, in addition to the studies that deal with this problem as a whole [22][23][24][25][26], there are also studies [12,13,[27][28][29][30][31][32][33][34] that have brought separate solutions to different parts of the problem and combined them [10,11,35]. In addition, there are also studies [36,37] that have attempted to solve the product recognition problem by recognizing the text on the product instead of recognizing it from the features of the image. On the other hand, the product recognition problem on the market shelves is also an object detection and object recognition problem.…”

Section: Related Workmentioning

confidence: 99%

“…The other end-to-end studies [36,37] attempted to solve the product recognition problem by recognizing the text on products. The aim of these studies was to improve shopping experience, especially for visually impaired peoples.…”

Section: Multi-stage End-to-end Product Recognitionmentioning

confidence: 99%

“…The proposed method enhances the effectiveness of the existing techniques. In a two-stage study [37], the Deep Belief Network (DBN) was used for selecting the images that were not captioned, and Bald Eagle Search (BES) was used to generate text according to products. The suggested technique outperformed the existing classifiers in terms of precision, recall, accuracy, and success rate while having a lower MSE value.…”

Section: Multi-stage End-to-end Product Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

Development of a Hybrid Method for Multi-Stage End-to-End Recognition of Grocery Products in Shelf Images

Melek,

Battini Sonmez,

Ayral

et al. 2023

Electronics

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Product recognition on grocery shelf images is a compelling task of object detection because of the similarity between products, the presence of the different scale of product sizes, and the high number of classes, in addition to constantly renewed packaging and added new products’ difficulty in data collection. The use of conventional methods alone is not enough to solve a number of retail problems such as planogram compliance, stock tracking on shelves, and customer support. The purpose of this study is to achieve significant results using the suggested multi-stage end-to-end process, including product detection, product classification, and refinement. The comparison of different methods is provided by a traditional computer vision approach, Aggregate Channel Features (ACF) and Single-Shot Detectors (SSD) are used in the product detection stage, and Speed-up Robust Features (SURF), Binary Robust Invariant Scalable Key points (BRISK), Oriented Features from Accelerated Segment Test (FAST), Rotated Binary Robust Independent Elementary Features (BRIEF) (ORB), and hybrids of these methods are used in the product classification stage. The experimental results used the entire Grocery Products dataset and its different subsets with a different number of products and images. The best performance was achieved with the use of SSD in the product detection stage and the hybrid use of SURF, BRISK, and ORB in the product classification stage, respectively. Additionally, the proposed approach performed comparably or better than existing models.

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