Authors:
Chuen-Min Huang
;
Yi-Ling Chuang
;
Rih-Wei Chang
and
Ya-Yun Chen
Affiliation:
National Yunlin University of Science and Technology, Taiwan
Keyword(s):
Image Pre-processing, Text Recognition, Optical Character Recognition (OCR), M-Library Services, Mobile Devices.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Facing the popularity of web searching, libraries continuously invest in the provision of online searching
and refurnish physical facilities to attract users during the past decades. In this study, we conducted a
technical feasibility study to facilitate library services by applying a novel image pre-processing technique
to enhance performance of OCR via mobile devices. In the binarization stage, a grayscale image is usually
assigned a global threshold value to be binary, while this will not be suitable for some scenarios, such as
non-uniform lightness and complicated background. Instead of segregating the grayscale image into many
regions like other studies, our approach only partitioned an image into three equal-sized horizontal segments
to identify the local threshold value of each segment and then restored the three segments back to the
original state. The experimental results illustrate that the proposed method efficiently and effectively
improves the text recognition. The accur
acy rate was raised from 17.7% to 72.05% of all test images.
Without counting eight unrecognizable images, the average accuracy rates of our treatment can reach
90.06%. To compare with other studies we conducted another evaluation to examine the validity of our
approach. The result showed that our treatment outperforms most of the other studies and the performance
achieves 74.6% in precision and 80.2% in the recall.We are confident that this design will not only bring
users more convenience in using libraries but help library staff and businessmen to manage the status of
books.
(More)