Misspelling Detection from Noisy Product Images (2020)

Abstract: Misspellings are introduced on products either due to negligence or as an attempt to deliberately deceive stakeholders. This leads to a revenue loss for online sellers and fosters customer mistrust. Existing spelling research has primarily focused on advancement in misspelling correction and the approach for misspelling detection has remained the use of a large dictionary. The dictionary lookup results in the incorrect detection of several non-dictionary words as misspellings. In this paper, we propose a method to automatically detect misspellings from product images in an attempt to reduce false positive detections. We curate a large scale corpus, define a rich set of features and propose a novel model that leverages importance weighting to account for within class distributional variance. Finally, we experimentally validate this approach on both the curated corpus and an out-of-domain public dataset and show that it leads to a relative improvement of up to 20% in F1 score. The approach thus creates a more robust, generalized deployable solution and reduces reliance on large scale custom dictionaries used today.

## introduction misspellings often occur on printed descriptions, warranty cards and advertisements associated with products despite the ubiquitous presence of spell checkers. although these misspellings are often due to human typographical errors @xcite , or by non-native writers who are confused by language intricacies, they may also be deliberately introduced by malicious elements to deceive stakeholders @xcite . a single inadvertent or intentional misspelling can propagate to large amounts of inventory. as a consequence and much to the chagrin of sellers, misspellings cost millions in lost online sales @xcite and breaks customer trust. this paper aims to automatically and accurately identify misspellings from product images in order to improve customer experience. we loosely define a misspelling as "incorrectly spelled ubiquitous words and proper nouns which are very similar to their correct forms". example images and words are present in figure 1 and leveraging existing approaches for misspelling detection from product images is beset with a number of challenges. first, although spelling research has intrigued the nlp community for long @xcite , misspelling detection research @xcite @xcite ) is very sparse, language specific and the primary approach has remained a dictionary lookup. this approach does not scale or generalize to billions of product images leading to a large number of false positive detections. second, unlike most language modeling tasks, the use of contextual cues is ineffective. as a misspelling might be deliberate, its occurrence is unlikely to be influenced by its neighbouring words. further, due to varied image quality and orientation, the extracted words may be scattered and not in the order they appear on the image. and third, to the best of our knowledge, there is no publicly available large scale corpus of product images annotated for misspellings. to address these deficiencies we make the following contributions. ## related work misspelling detection research is very limited, small scale and often on domain specific private data @xcite . approaches for misspelling detection primarily involve use of a predefined dictionary of n-grams @xcite and words @xcite @xcite . additionally, dictionaries used are limited to specific languages like chinese @xcite , turkish @xcite and arabic @xcite . most existing work on spelling has focused on spelling error correction @xcite @xcite @xcite @xcite @xcite . initial approaches for correcting misspelling include those that compute edit distance and phonetic similarity between misspelling and candidate correction. consequently, @xcite introduced an approach to combine ranking candidate corrections using contextual cues with edit distance and phonetic similarity. although several datasets exist for spelling correction research, many of these are small and proprietary corpora. @xcite proposed toefl-spell as the first large scale corpus to benchmark spelling correction performance. spelling research is transitioning to leverage word embeddings with the advent of deep learning. since pretrained word embeddings like word2vec @xcite and glove @xcite do not support out-of-vocabulary (oov) words such as a misspelling, @xcite and @xcite introduced approaches for obtaining word embeddings resilient to misspellings. however, misspelling embeddings leverage context during training and have been designed to be close to their correct variants. this is counter intuitive to our task where we would like to disentangle misspellings from non misspellings and the occurrence of a misspelling is unlikely to be influenced by its neighbouring words present on product images. ## problem formulation given an image i consisting of n words, and an ocr engine o which extracts words w i from the image not necessarily in the order they appear: a word w is characterized as a misspelling if it loosely satisfies one or more of the following conditions: ## proposed approach we now describe the process of data curation, feature extraction and model learning. ## conclusion and future directions this paper addressed the problem of automatic detection of misspellings from product images. we presented image-misspell, a large dataset of product images augmented with public corpora, exhaustively annotated for misspelling detection. we also defined a rich set of features that can be extracted from words without context. further, we proposed a cost sensitive weighted svm model that leverages importance weighting to account for the within class distributional variation. finally through experiments we demonstrated the enhanced performance of this model in relation to baseline approaches. the approach is promising since it achieves better results with a lower time, monetary and human capital investment. future directions include leveraging deep learning where character level language modeling could be used to learn embeddings that disentangle misspellings from their correct form. visual features like pyramidal histogram of characters (phoc) @xcite can also provide important cues for ocr errors and deliberate misspellings.