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Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR)
The impact of apartheid on land registration is still evident within South Africa. The Deeds Registry is facing a current backlog in registering an estimated 900,000 title deeds. Providing formal ownership, through title, is seen as necessary for unlocking the 'dead capital’ of unregistered property...
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| Format: | Thesis |
| Language: | English |
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African Institute of Financial Markets and Risk Management
2020
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| _version_ | 1867613222487457792 |
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| access_status_str | Open Access |
| author | Favish, Ashleigh |
| author2 | Georg, Co-Pierre |
| author_browse | Favish, Ashleigh Georg, Co-Pierre |
| author_facet | Georg, Co-Pierre Favish, Ashleigh |
| author_sort | Favish, Ashleigh |
| collection | Thesis |
| description | The impact of apartheid on land registration is still evident within South Africa. The Deeds Registry is facing a current backlog in registering an estimated 900,000 title deeds. Providing formal ownership, through title, is seen as necessary for unlocking the 'dead capital’ of unregistered property, fostering access to capital markets and poverty alleviation. Within the current legislative framework, the Deeds Registry only accepts paper documents, which introduces inefficiencies. To increase the number of deeds processed per day, automation of manual data capture is tested using an OCR pipeline. To adapt to the linguistics used in title deeds, text analysis and parsing is done using Regex. Uploading the scanned title deeds onto IPFS is as an additional security measure included in the pipeline. Previous research has failed to apply these techniques to formal land registration or other South African government institutions. The preliminary results show that this pipeline has an overall accuracy of 89.6%. This represents the comparison of the expected output to the output extracted using OCR. The results are significantly less accurate when classifying handwritten and stamped information. Thus, further measures are required to increase accuracy for these fields. The OCR accuracy was 98.3% for the fields extracted from typed text characters. This is within the accuracy range of manual data capture. A secondary quality check, which is currently done on manual data capture, would still be necessary to ensure accuracy of inputs. Overall it appears that this application would be appropriate for incorporation into the Deeds Registry to streamline their processes while ensuring title deed validity. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31389 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:42.829Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | African Institute of Financial Markets and Risk Management |
| publisherStr | African Institute of Financial Markets and Risk Management |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31389 Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR) Favish, Ashleigh Georg, Co-Pierre Financial Technology The impact of apartheid on land registration is still evident within South Africa. The Deeds Registry is facing a current backlog in registering an estimated 900,000 title deeds. Providing formal ownership, through title, is seen as necessary for unlocking the 'dead capital’ of unregistered property, fostering access to capital markets and poverty alleviation. Within the current legislative framework, the Deeds Registry only accepts paper documents, which introduces inefficiencies. To increase the number of deeds processed per day, automation of manual data capture is tested using an OCR pipeline. To adapt to the linguistics used in title deeds, text analysis and parsing is done using Regex. Uploading the scanned title deeds onto IPFS is as an additional security measure included in the pipeline. Previous research has failed to apply these techniques to formal land registration or other South African government institutions. The preliminary results show that this pipeline has an overall accuracy of 89.6%. This represents the comparison of the expected output to the output extracted using OCR. The results are significantly less accurate when classifying handwritten and stamped information. Thus, further measures are required to increase accuracy for these fields. The OCR accuracy was 98.3% for the fields extracted from typed text characters. This is within the accuracy range of manual data capture. A secondary quality check, which is currently done on manual data capture, would still be necessary to ensure accuracy of inputs. Overall it appears that this application would be appropriate for incorporation into the Deeds Registry to streamline their processes while ensuring title deed validity. 2020-02-28T11:46:12Z 2020-02-28T11:46:12Z 2019 2020-02-28T11:09:38Z Master Thesis Masters MPhil http://hdl.handle.net/11427/31389 eng application/pdf African Institute of Financial Markets and Risk Management Faculty of Commerce |
| spellingShingle | Financial Technology Favish, Ashleigh Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR) |
| thesis_degree_str | Master's |
| title | Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR) |
| title_full | Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR) |
| title_fullStr | Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR) |
| title_full_unstemmed | Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR) |
| title_short | Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR) |
| title_sort | data capture automation in the south african deeds registry using optical character recognition ocr |
| topic | Financial Technology |
| url | http://hdl.handle.net/11427/31389 |
| work_keys_str_mv | AT favishashleigh datacaptureautomationinthesouthafricandeedsregistryusingopticalcharacterrecognitionocr |