Research of the Books Localization Algorithm in Library Intelligent Bookshelf System Based on RFID

Shu, Yuanzhong; Su, Yannan; Peng, Xiaohong; Mei, Mengzhe

doi:10.2991/scict-14.2014.12

Cited by 1 publication

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been studies of book management. Shu et al [2] proposed an accurate location algorithm based on an RFID intelligent bookshelf. Although the proposed al- gorithm improved the positioning accuracy, it naturally supposed a number of RFIDs were available, which can be costly.…”

Section: Related Workmentioning

confidence: 99%

“…Both of the learning-based models we used in this study were trained with ICDAR 2015 data [17], which were different from our target application domain. R 2 CNN and EAST we used in this work were trained with ICDAR 2015 data [17], which were in a different domain from our target, i.e., images of roughly placed books. An application way of such a learning-based approach is to train it with a considerable amount of data in the target domain.…”

Section: Related Workmentioning

confidence: 99%

“…It is required to automatically manage a lot of products, such as books and packing boxes. Although some additional equipment including bar codes and RFID tags is often used for the management [2], it may require us to cost too much. Some texts are supposed to be printed on any products to be managed, and this study intends to detect the texts from an image of products automatically and use for management.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Text Detection on Books Using CNN Trained with Another Domain Data

Anegawa¹,

Aritsugi²

2019

2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

View full text Add to dashboard Cite

Text detection enables us to extract rich information from images. In this paper, we focus on how to generate bounding boxes that are appropriate to grasp text areas on books to help implement automatic text detection. We attempt not to improve a learning-based model by training it with an enough amount of data in the target domain but to leverage it, which has been already trained with another domain data. We develop algorithms that construct the bounding boxes by improving and leveraging the results of a learning-based method. Our algorithms can utilize different learning-based approaches to detect scene texts. Experimental evaluations demonstrate that our algorithms work well in various situations where books are roughly placed.There have been many studies of identifying books and text detection on them [3,4,5,6]. For example, [6] exploited line extractions based on the Hough transform. However, such work may fail to find text areas exactly when a book has a title of many words. Recently, text detection from natural scenes has intensely been developed as learning-based methods [7,8,9,10,11,12,13]. We consider leveraging learning-based methods for detecting texts in natural scene images in the text detection on books.Note that we do not attempt to improve the learning-based methods by training them with a good set of data but adopt them with additional refinement processes. This intention comes from the fact that it is often difficult to collect enough amount of data in the target domain and make them to be used for training. Recently, transfer learning has intensely been studied [14,15]. Transfer learning is the technology that re-learns only a part of the parameters of a model trained in another domain. In this paper, we exploit trained models instead of re-learning. We develop algorithms that construct bounding boxes for text areas by improving and leveraging the results of a learning-based method.This paper is based on our previous work [1], in which a convolutional neural network (CNN)-based method called R 2 CNN [8] was used to develop concrete discussions. In this paper, we additionally use another learning-based method called EAST [7], which is based on a fully convolutional network model and achieves a fast and accurate scene text detection pipeline. Figure 1 shows an overview of our study where an image is processed first by a learning-based method, i.e., R 2 CNN or EAST, and the result is then processed by our proposal to get the final result of text detection.The main contributions of this paper are as follows:

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation