Comparison of approaches for mobile document image analysis using server supported smartphones

Date

2014-02-05

Author

Ozarslan, Suleyman
Eren, Pekin Erhan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

203
views

0
downloads

With the recent advances in mobile technologies, new capabilities are emerging, such as mobile document image analysis. However, mobile phones are still less powerful than servers, and they have some resource limitations. One approach to overcome these limitations is performing resource-intensive processes of the application on remote servers. In mobile document image analysis, the most resource consuming process is the Optical Character Recognition (OCR) process, which is used to extract text in mobile phone captured images. In this study, our goal is to compare the in-phone and the remote server processing approaches for mobile document image analysis in order to explore their trade-offs. For the in-phone approach, all processes required for mobile document image analysis run on the mobile phone. On the other hand, in the remote-server approach, core OCR process runs on the remote server and other processes run on the mobile phone. Results of the experiments show that the remote server approach is considerably faster than the in-phone approach in terms of OCR time, but adds extra delays such as network delay. Since compression and downscaling of images significantly reduce file sizes and extra delays, the remote server approach overall outperforms the in-phone approach in terms of selected speed and correct recognition metrics, if the gain in OCR time compensates for the extra delays. According to the results of the experiments, using the most preferable settings, the remote server approach performs better than the in-phone approach in terms of speed and acceptable correct recognition metrics.

Subject Keywords

Mobile, Remote server approach, In-phone approach, Downscaling, Compression , OCR

URI

https://hdl.handle.net/11511/30589

DOI

https://doi.org/10.1117/12.2040687

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

An Efficient graph-theoretical approach for interactive mobile image and video segmentation Şener, Ozan; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2013) Over the past few years, processing of visual information by mobile devices getting more affordable due to the advances in mobile technologies. Efficient and accurate segmentation of objects from an image or video leads many interesting multimedia applications. In this study, we address interactive image and video segmentation on mobile devices. We first propose a novel interaction methodology leading better satisfaction based on subjective user evaluation. Due to small screens of the mobile devices, error ...
Comparison of cognitive modeling and user performance analysis for touch screen mobile interface design Ocak, Nihan; Çağıltay, Kürşat; Department of Information Systems (2014) The main aim of this thesis is to analyze and comparatively evaluate the usability of touch screen mobile applications through cognitive modeling and end-user usability testing. The study investigates the accuracy of the estimated results cognitive model produces for touch screen mobile phone interfaces. CogTool application was used as the cognitive modeling method. Turkcell Cüzdan application, which is suitable for the implementation of both methods, was chosen as the mobile application. Based on the feedb...
Fine‐grained recognition of maritime vessels and land vehicles by deep feature embedding Solmaz, Berkan; Gundogdu, Erhan; Yucesoy, Veysel; Koc, Aykut; Alatan, Abdullah Aydın (2018-12-01) Recent advances in large-scale image and video analysis have empowered the potential capabilities of visual surveillance systems. In particular, deep learning-based approaches bring in substantial benefits in solving certain computer vision problems such as fine-grained object recognition. Here, the authors mainly concentrate on classification and identification of maritime vessels and land vehicles, which are the key constituents of visual surveillance systems. Employing publicly available data sets for ma...
Exploring behavior change features for mobile workout applications ÜNAL, Perin; Cavdar, Seyma Kucukozer; Taşkaya Temizel, Tuğba; Eren, Pekin Erhan; IYENGAR, Sriram (2017-08-23) With the rapid emergence of mobile technologies in recent years, mobile health (m-health) has become fundamental to healthcare. Persuasion strategies and behavior change support features are widely used in m-health applications to increase the effectiveness of these applications on users. However, in the literature, there is a lack of research to analyze the current situation of m-health applications particularly from the perspective of behavior change approaches. In this study, the workout applications in ...
An Exploratory Study on the Outcomes of Influence Strategiesin Mobile Application Recommendations Ünal, Perin; Taşkaya Temizel, Tuğba; Eren, Pekin Erhan (null; 2014-05-23) The rapid growth in the mobile application market presents a significant challenge to find interesting and relevant applications for users. Recommendation systems deal with ends such as movies and consumer goods that are consumed by users where similarity between consumer tastes is generally taken into account. On the other hand, recommendation systems for mobile applications differ from traditional systems in terms of the characteristics of the ends they recommend. They present applications that are not ju...

Citation Formats

S. Ozarslan and P. E. Eren, “Comparison of approaches for mobile document image analysis using server supported smartphones,” 2014, vol. 9023, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30589.