The area of dietary assessment is becoming increasingly important as obesity rates soar, but valid measurement of the
food intake in free-living persons is extraordinarily challenging. Traditional paper-based dietary assessment methods
have limitations due to bias, user burden and cost, and therefore improved methods are needed to address important
hypotheses related to diet and health. In this paper, we will describe the progress of our mobile Diet Data Recorder
System (DDRS), where an electronic device is used for objective measurement on dietary intake in real time and at
moderate cost. The DDRS consists of (1) a mobile device that integrates a smartphone and an integrated laser package,
(2) software on the smartphone for data collection and laser control, (3) an algorithm to process acquired data for food
volume estimation, which is the largest source of error in calculating dietary intake, and (4) database and interface for
data storage and management. The estimated food volume, together with direct entries of food questionnaires and voice
recordings, could provide dietitians and nutritional epidemiologists with more complete food description and more
accurate food portion sizes. In this paper, we will describe the system design of DDRS and initial results of dietary
assessment.
Post-processing of OCR is a bottleneck of the document image processing system. Proof reading is necessary since the
current recognition rate is not enough for publishing. The OCR system provides every recognition result with a confident
or unconfident label. People only need to check unconfident characters while the error rate of confident characters is low
enough for publishing. However, the current algorithm marks too many unconfident characters, so optimization of OCR
results is required. In this paper we propose an algorithm based on pattern matching to decrease the number of
unconfident results. If an unconfident character matches a confident character well, its label could be changed into a
confident one. Pattern matching makes use of original character images, so it could reduce the problem caused by image
normalization and scanned noises. We introduce WXOR, WAN, and four-corner based pattern matching to improve the
effect of matching, and introduce confidence analysis to reduce the errors of similar characters. Experimental results
show that our algorithm achieves improvements of 54.18% in the first image set that contains 102,417 Chinese
characters, and 49.85% in the second image set that contains 53,778 Chinese characters.
The JBIG2 (joint bi-level image group) standard for bi-level image coding is drafted to allow encoder designs by individuals. In JBIG2, text images are compressed by pattern matching techniques. In this paper, we propose a lossy text image compression method based on OCR (optical character recognition) which compresses bi-level images into the JBIG2 format. By processing text images with OCR, we can obtain recognition results of characters and the confidence of these results. A representative symbol image could be generated for similar character image blocks by OCR results, sizes of blocks and mismatches between blocks. This symbol image could replace all the similar image blocks and thus a high compression ratio could be achieved. Experiment results show that our algorithm achieves improvements of 75.86% over lossless SPM and 14.05% over lossy PM and S in Latin Character images, and 37.9% over lossless SPM and 4.97% over lossy PM and S in Chinese character images. Our algorithm leads to much fewer substitution errors than previous lossy PM and S and thus preserves acceptable decoded image quality.
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