  Word error rate of an ocr is often higher than its character error rate. This
is especially true when ocrs are designed by recognizing characters. High word
accuracies are critical to tasks like the creation of content in digital
libraries and text-to-speech applications. In order to detect and correct the
misrecognised words, it is common for an ocr module to employ a post-processor
to further improve the word accuracy. However, conventional approaches to
post-processing like looking up a dictionary or using a statistical language
model (slm), are still limited. In many such scenarios, it is often required to
remove the outstanding errors manually. We observe that the traditional
post-processing schemes look at error words sequentially since ocrs process
documents one at a time. We propose a cost-efficient model to address the error
words in batches rather than correcting them individually. We exploit the fact
that a collection of documents, unlike a single document, has a structure
leading to repetition of words. Such words, if efficiently grouped together and
corrected as a whole can lead to a significant reduction in the cost.
Correction can be fully automatic or with a human in the loop. Towards this, we
employ a novel clustering scheme to obtain fairly homogeneous clusters. We
compare the performance of our model with various baseline approaches including
the case where all the errors are removed by a human. We demonstrate the
efficacy of our solution empirically by reporting more than 70% reduction in
the human effort with near perfect error correction. We validate our method on
Books from multiple languages.
