A hand-annotated dataset of 1933 Hamilton County property appraisals is extracted with template-aligned OCR, and a random forest trained on contemporary features estimates those historical values with about 17% MAPE.
A Conglomerate of Multiple OCR Table Detection and Extraction
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abstract
Information representation as tables are compact and concise method that eases searching, indexing, and storage requirements. Extracting and cloning tables from parsable documents is easier and widely used, however industry still faces challenge in detecting and extracting tables from OCR documents or images. This paper proposes an algorithm that detects and extracts multiple tables from OCR document. The algorithm uses a combination of image processing techniques, text recognition and procedural coding to identify distinct tables in same image and map the text to appropriate corresponding cell in dataframe which can be stored as Comma-separated values, Database, Excel and multiple other usable formats.
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Predicting the Past: Estimating Historical Appraisals with OCR and Machine Learning
A hand-annotated dataset of 1933 Hamilton County property appraisals is extracted with template-aligned OCR, and a random forest trained on contemporary features estimates those historical values with about 17% MAPE.