Probabilistic Management of OCR Data using an RDBMS

TL;DR

This paper introduces a probabilistic approach to manage OCR data within relational databases, retaining uncertainty to improve query accuracy, and proposes an approximation scheme called Staccato for balancing performance and recall.

Contribution

The paper presents a novel method for integrating probabilistic OCR outputs into RDBMS, including an approximation scheme that balances recall and performance, with formal analysis and indexing integration.

Findings

Staccato significantly improves query performance while maintaining high recall.

Probabilistic OCR data integration enhances retrieval accuracy over traditional ASCII storage.

The scheme is formally analyzed and effectively integrated with standard RDBMS indexing.

Abstract

The digitization of scanned forms and documents is changing the data sources that enterprises manage. To integrate these new data sources with enterprise data, the current state-of-the-art approach is to convert the images to ASCII text using optical character recognition (OCR) software and then to store the resulting ASCII text in a relational database. The OCR problem is challenging, and so the output of OCR often contains errors. In turn, queries on the output of OCR may fail to retrieve relevant answers. State-of-the-art OCR programs, e.g., the OCR powering Google Books, use a probabilistic model that captures many alternatives during the OCR process. Only when the results of OCR are stored in the database, do these approaches discard the uncertainty. In this work, we propose to retain the probabilistic models produced by OCR process in a relational database management system. A key technical challenge is that the probabilistic data produced by OCR software is very large (a single book blows up to 2GB from 400kB as ASCII). As a result, a baseline solution that integrates these models with an RDBMS is over 1000x slower versus standard text processing for single table select-project queries. However, many applications may have quality-performance needs that are in between these two extremes of ASCII and the complete model output by the OCR software. Thus, we propose a novel approximation scheme called Staccato that allows a user to trade recall for query performance. Additionally, we provide a formal analysis of our scheme's properties, and describe how we integrate our scheme with standard-RDBMS text indexing.