Automatic Book Spine Extraction and Recognition for Library Inventory Management

Nevetha, M. P.; Baskar, A.

doi:10.1145/2791405.2791506

Cited by 13 publications

(4 citation statements)

References 3 publications

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“…While their system offers an acceptable latency of 1 s, even with low computing power, only a small part of a bookcase can be analyzed at once and only if all of the books have the same vertical orientation. Nevetha and Baskar [27] use a line segment detector [37] to also detect slightly inclined books up to an angle of 15°. In a test shelf of 20 books, 11 books could be successfully identified.…”

Section: Visual Searchmentioning

confidence: 99%

Augmenting Public Bookcases to Support Book Sharing

Schrapel

Schulz

Rohs

2020

22nd International Conference on Human-Computer Interaction With Mobile Devices and Services

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Public bookcases offer the opportunity to serendipitously discover books and to anonymously share books with others. The set of available books as well as the sharing patterns are highly dynamic, as anybody can freely take or donate books. This makes it difficult for users to see what is available or of interest to them. To support book sharing via public bookcases we developed a mobile AR application that highlights relevant books in the camera viewfinder and that facilitates searching for specific books. The application recognizes books via text and color features on the spine. In a lab study with 15 participants we evaluated our book recognition algorithm and found that it outperforms unaided visual search. We interviewed users of public bookcases and analyzed the bookcases' setup and rate of change. A subsequent field evaluation of the AR application on nine public bookcases found a recognition accuracy of 80 % for 450 books under different conditions. The proposed approach provides the basis for effectively sharing books via public bookcases. CCS CONCEPTS • Information systems → Mobile information processing systems; • Human-centered computing → Mixed / augmented reality; Interaction techniques.

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Section: Visual Searchmentioning

confidence: 99%

Augmenting Public Bookcases to Support Book Sharing

Schrapel

Schulz

Rohs

2020

22nd International Conference on Human-Computer Interaction With Mobile Devices and Services

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“…A general framework of book spine extraction was proposed by Quoc and Choi (2009), and several challenges were addressed, such as lighting condition and distortion of images. Systems have been also designed for reading book spines including that of Chen et al (2010); Nevetha and Baskar (2015); Lee et al (2008); Tsai et al (2011). In Taira, Uchida, and Sakoe (2003), a finite state automata model for book boundary detection, combined with a global model-based optimization algorithm, was formulated for detecting the angle and boundary of each book.…”

Section: Related Workmentioning

confidence: 99%

Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading

Xiao¹,

He²,

Huang³

et al. 2017

2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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Physical library collections are valuable and long standing resources for knowledge and learning. However, managing books in a large bookshelf and finding books on it often leads to tedious manual work, especially for large book collections where books might be missing or misplaced. Recently, deep neural models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have achieved great success for scene text detection and recognition. Motivated by these recent successes, we aim to investigate their viability in facilitating book management, a task that introduces further challenges including large amounts of cluttered scene text, distortion, and varied lighting conditions. In this paper, we present a library inventory building and retrieval system based on scene text reading methods. We specifically design our scene text recognition model using rich supervision to accelerate training and achieve state-of-the-art performance on several benchmark datasets. Our proposed system has the potential to greatly reduce the amount of human labor required in managing book inventories as well as the space needed to store book information.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

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

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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:

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Automatic Book Spine Extraction and Recognition for Library Inventory Management

Cited by 13 publications

References 3 publications

Augmenting Public Bookcases to Support Book Sharing

Augmenting Public Bookcases to Support Book Sharing

Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading

Text Detection on Books Using CNN Trained with Another Domain Data

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