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Using OCR for Census Data Capture in China National Bureau of Statistics of China

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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Using OCR for Census Data Capture in China National Bureau of Statistics of China

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  1. Using OCR for Census Data Capture in China National Bureau of Statistics of China

  2. 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. • 1982,1990 census, manual data entry • 2000 census, using OCR for data capture • 2006, the second Agriculture census: also use OCR for data capture 2000 population census and 2006 agriculture census are the two cases of OCR use for large-volume data capture

  3. Two cases of OCR for large-volume Census Data Capture • The data capture of 2000 Population Census • Census reference time: Nov. 1,2000 • Data Capture cycle：Jan. -- June., 2001.(6 months) • Scale： • Types of Census Form：4 • Short Form: 49 Census items, 90% HH, 360 million A4 size double sheets in total • Long Form: 95 Census items, 10% HH, 40 million A3 double sheets in total • Other Forms: Death pop, temporary residents. 10 million A4 double sheets in total • Original Census data：About 64 GB • Image volume: 5.5TB

  4. Two cases of OCR for large-volumes Census Data Capture • The Second National Agricultural Census • Census reference time: end of 2006 • Data Capture cycle ：April to mid-July, 2007,100 days • Scale： • Types of Census Form：8 • Total census items：541 • Total agricultural Families：250 millions • Total Census Forms：about 500 million pieces of paper • Original Census data ：about 300GB • Image data ：40TB

  5. Organizational Structure for data process Data process Data Capture editing NBS Province Coding 31 Checked & packed Prefecture Checked & packed 340 Checked & packed 1 3 1 County Data capture was decentralized at prefecture offices Town 2847 Village 40000 0.9 million EA 5 million

  6. Function framework of OCR data capture(2006 agriculture census) User management System Initialization System management Log management Space management Sys management Address base Client management task management Archiving management process management QA Scanner self-inspection Forms check scanning Batchform scan Add scan Alternative scan Repeat scan System Functions Check scan Chinese character numeric data English character OCR Special character Chinese characteredit Generation census form management ID checkup numeric data edit Editing and checkup English character edit Special character edit Generation image management ID Backup Restore Delete Data management Browse Input Output Backup Restore Delete Enquiry Browse Image management Import export Image merger Image reported Receiving file Statistics summary Progress monitoring Information display

  7. The Process of OCR data capture • The scanning module generates image files and transmits them to image management module and also transmits the status information to task management module. • The task management module executes task distribution according to the state of vacancy of each OCR clients.

  8. The Process of OCR data capture • The OCR module performs recognition of numerical data and Chinese characters and transmits the data and Chinese characters to data management module and transmits the status information to task management module.

  9. The Process of OCR data capture • The task management dispatches the data to edit module for editing. If original image is needed, corresponding image is fetched by image management module for comparison, the cleansed data after edit are returned back to data management module. when data capture work is all finished, report upward the data.

  10. Quality Control To ensure the quality of captured data, quality control is executed in three stages: scanning, recognizing and data editing. • During the process of scanning, recognizing batch cover data and scanner count, the system checks if the total page count, total household count for each batch are consistent with the results of scanning; Comparing the actual address code with address code repository, ensure that the address codes are validity, uniqueness and correctness. • During the recognition, collecting real time statistics for rejection ratio and suspect ratio. If rejection ratio and suspect ratio is too high, the task administrator checks the reason.

  11. Quality Control • During the process of editing, checking the consistency between recognized record count and the record count in controller document; Checking the basic logic relationship and value range; indicate the items which have mistakes in logic relationships or value ranges, recognition results and corresponding items from original scanned images are displayed comparatively in parallel windows, and convenient modification means are provided for those which need get modified. • After the whole set of data has been captured, quality is assured through executing sampling quality check through all phases

  12. Main Problems and Solutions 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.

  13. Main Problems and Solutions 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.

  14. Main Problems and Solutions 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.

  15. Lessons Learned • Using advanced technology to raise efficiency • Combining technical and administrative methods to resolve quality problems and security issues • Choose partners with the higher capability of system development and service • Early project preparation • Manage project with partners • Training, pilot projects and management is the key to success • Control the printing quality of the census forms and census data filling quality • Project change control

  16. Prospect of the 2010 Population Census Census time: Nov. 1, 2010 • Short form and long form, death population form • Foreigners living in China are considered to be enumerated Data capture in 2011 • OCR data capture will be the main data entry method • Modifying the existing system of agricultural census and make some innovate • Adding more OCR equipments

  17. THANKS

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