Probabilistic Reverse Annotation for Large Scale Image Retrieval

Sankar, K. Pramod; Jawahar, C. V.

doi:10.1109/cvpr.2007.383169

Cited by 18 publications

(14 citation statements)

References 16 publications

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“…On the other hand, recognition-free approaches such as Word Spotting were proposed to enable search over handwritten [3,28,30] and printed documents [4,13,26]. Pramod and Jawahar [26] use an automatic annotation approach to assign text labels to a large collection of document images.…”

Section: Previous Workmentioning

confidence: 99%

“…Our approach has been motivated by work in word spotting in handwriting [28,30] and print [4,13,26]. Word spotting approaches usually match entire word-images.…”

Section: Recognizing Document Collec-tionsmentioning

confidence: 99%

“…Pramod and Jawahar [26] use an automatic annotation approach to assign text labels to a large collection of document images. However, they annotate for only those words that are considered useful for retrieval.…”

Section: Previous Workmentioning

confidence: 99%

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Nearest neighbor based collection OCR

Sankar

Jawahar

Manmatha

2010

Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

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Conventional optical character recognition (OCR) systems operate on individual characters and words, and do not normally exploit document or collection context. We describe a Collection OCR which takes advantage of the fact that multiple examples of the same word (often in the same font) may occur in a document or collection. The idea here is that an OCR or a reCAPTCHA like process generates a partial set of recognized words. In the second stage, a nearest neighbor algorithm compares the remaining word-images to those already recognized and propagates labels from the nearest neighbors. It is shown that by using an approximate fast nearest neighbor algorithm based on Hierarchical K-Means (HKM), we can do this accurately and efficiently. It is also shown that profile based features perform much better than SIFT and Pyramid Histogram of Gradient (PHOG) features. We believe that this is because profile features are more robust to word degradations (common in our documents). This approach is applied to a collection of Telugu booksa language for which no commercial OCR exists. We show from a selection of 33 Telugu books that starting with OCR labels for only 30% of the collection we can recognize the remaining 70% of the words in the collection with 70% accuracy using this approach. Since the approach makes no language specific assumptions, it should be applicable to a large number of languages. In particular we are interested in its applicability to Indic languages and scripts.

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Section: Previous Workmentioning

confidence: 99%

“…Our approach has been motivated by work in word spotting in handwriting [28,30] and print [4,13,26]. Word spotting approaches usually match entire word-images.…”

Section: Recognizing Document Collec-tionsmentioning

confidence: 99%

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Nearest neighbor based collection OCR

Sankar

Jawahar

Manmatha

2010

Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

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“…However, there are several ways to speed up image search engines. One option is to quantize and/or index image features and retrieve them whenever necessary [2], [9], [4], [11]. Another common practice is to gain speed by sacrificing retrieval accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Framework for Searching Text in Noisy Document Images

Yalnız

Manmatha

2012

2012 10th IAPR International Workshop on Document Analysis Systems

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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.

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“…[7,8] focused on searching handwritten word images. Dynamic time warping (DTW) [7] was shown to be a good solution and was exploited by [9] to index a collection of 500 scanned printed books in Indian languages. The one weakness of dynamic time warping is that it is slow and hence [3] showed that locality sensitive hashing could instead be used to index and search word images rapidly within a book.…”

Section: Introductionmentioning

confidence: 99%

Robust Recognition of Documents by Fusing Results of Word Clusters

Rasagna¹,

Kumar²,

Jawahar³

et al. 2009

2009 10th International Conference on Document Analysis and Recognition

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The word error rate of any optical character recognition system (OCR) is usually substantially below its component or character error rate. This is especially true of Indic languages in which a word consists of many components. Current OCRs recognize each character or word separately and do not take advantage of document level constraints. We propose a document level OCR which incorporates information from the entire document to reduce word error rates. Word images are first clustered using a locality sensitive hashing technique. Individual words are then recognized using a (regular) OCR. The OCR outputs of word images in a cluster are then corrected probabilistically by comparing with the OCR outputs of other members of the same cluster. The approach may be applied to improve the accuracy of any OCR run on documents in any language. In particular, we demonstrate it for Telugu, where the use of language models for post-processing is not promising. We show a relative improvement of 28% for long words and 12% for all words which appear at least twice in the corpus.

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Probabilistic Reverse Annotation for Large Scale Image Retrieval

Abstract: Abstract

Cited by 18 publications

References 16 publications

Nearest neighbor based collection OCR

Nearest neighbor based collection OCR

An Efficient Framework for Searching Text in Noisy Document Images

Robust Recognition of Documents by Fusing Results of Word Clusters

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