An efficient framework for searching text in noisy document images

I. Zeki Yalniz

Abstract:

An efficient word spotting framework is proposed
to search text in scanned books. The proposed method allows
one to search for words when optical character recognition
(OCR) fails due to noise or for languages where there is
no OCR. Given a query word image, the aim is to retrieve
matching words in the book sorted by the similarity. In the
offline stage, SIFT descriptors are extracted over the corner
points of each word image. Those features are quantized into
visual terms (visterms) using hierarchical K-Means algorithm
and indexed using an inverted file. In the query resolution
stage, the candidate matches are efficiently identified using
the inverted index. These word images are then forwarded
to the next stage where the configuration of visterms on the
image plane are tested. Configuration matching is efficiently
performed by projecting the visterms on the horizontal axis
and searching for the Longest Common Subsequence (LCS)
between the sequences of visterms. The proposed framework
is tested on one English and two Telugu books. It is shown
that the proposed method resolves a typical user query under
10 milliseconds providing very high retrieval accuracy (Mean
Average Precision 0.93). The search accuracy for the English
book is comparable to searching text in the high accuracy
output of a commercial OCR engine.