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Using OCR for Census Data Capture in China National Bureau of Statistics of China. Background. 5 population censuses have been conducted in 1953, 1964, 1982, 1990, 2000 respectively 1953,1964 census: manual tabulation Since 1982 census, using computer for data process.
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2000 population census and 2006 agriculture census are the two cases of OCR use for large-volume data capture
A4 size double sheets in total
A3 double sheets in total
million A4 double sheets in total
Data capture was decentralized at prefecture offices
Generation census form management ID
numeric data edit
Editing and checkup
English character edit
Special character edit
Generation image management ID
To ensure the quality of captured data, quality control is executed in three stages: scanning, recognizing and data editing.
In large-scale census data capture projects, there’re three aspects of problems we regard as the most outstanding: 1. How to enhance OCR’s recognition capability. 2. Availability and reliability of the system. 3. Project management. What we have done are:
1. Improve the capability of recognizing numeric characters
Two kinds of recognition algorithms and two kinds of recognition engines based on the two algorithms were developed, after a series of onsite test, which better suites the census project is chosen.
2. Improve the recognition capability for Chinese characters
By collecting large number of actual samples and training the recognizer, recognition capability for Chinese names is improved.
3. Improve orientation capability
Aiming at print deviation and filling deviation, smart locating algorithm has been developed which has minimized the impact of the print deviation and filling deviation.
4. Enhance efficiency of recognition
Improve the fundamental software of scanner, to achieve the best match between hardware drivers and OCR software and improve the efficiency of recognition.
5. Improve the quality of forms filling
Prescribe the filling standards for form filling so that OCR error rate will be reduced, meanwhile rejection rate could also be reduced.
6. Establish regulation, working guidance and processes to make every data entry site to execute work following uniform regulations, processes and standards.
7. Strengthen the training. we organized centralized training and on-site training for the users. Lecturing and actual operations are combined during centralized training, through the combination of these two ways, the familiarity with the system has get deepened.
8. Organize multi-target pilot. We organized multiple pilots in many locations aiming at different targets.
Census time: Nov. 1, 2010
Data capture in 2011