Shape Encoded Post Processing of Gurmukhi OCR

Sharma, Dharam Veer; Lehal, Gurpreet Singh; Mehta, Sarita

doi:10.1109/icdar.2009.180

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…The main idea behind key search techniques is to map the search string with the strings that has similar keys. Lehal [5] used key search techniques. When a key is computed for a misspelled string it will provide a pointer to all similarly spelled words (candidates) in the lexicon.…”

Section: 2key Similarity Techniquesmentioning

confidence: 99%

A Survey on Various OCR Errors

Kumar¹

2016

IJCA

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Section: 2key Similarity Techniquesmentioning

confidence: 99%

A Survey on Various OCR Errors

Kumar¹

2016

IJCA

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“…Although efforts have been present in developing postprocessing techniques for such inflectional languages [5], [6], it has been perceived and argued that most of the attempts in English have no direct impact on such languages. There are many valid reasons for these arguments: (a) the electronic resources available for many of these languages is very small (eg.…”

Section: Introductionmentioning

confidence: 99%

Error Detection in Highly Inflectional Languages

Sankaran

Jawahar

2013

2013 12th International Conference on Document Analysis and Recognition

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Abstract-Error detection in OCR output using dictionaries and statistical language models (SLMs) have become common practice for some time now, while designing post-processors. Multiple strategies have been used successfully in English to achieve this. However, this has not yet translated towards improving error detection performance in many inflectional languages, specially Indian languages. Challenges such as large unique word list, lack of linguistic resources, lack of reliable language models, etc. are some of the reasons for this. In this paper, we investigate the major challenges in developing error detection techniques for highly inflectional Indian languages. We compare and contrast several attributes of English with inflectional languages such as Telugu and Malayalam. We make observations by analyzing statistics computed from popular corpora and relate these observations to the error detection schemes. We propose a method which can detect errors for Telugu and Malayalam, with an F-Score comparable to some of the less inflectional languages like Hindi. Our method learns from the error patterns and SLMs.

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“…This technique is not applicable for some other Indian languages (like Telugu, Malayalam) due to the sheer size of their vocabulary. Sharma et al [18] have put forth shape encoding based post-processing for Gurumukhi (an Indian script). The consonants are grouped based on shape similarity and a number is assigned to each group or subset.…”

Section: Introductionmentioning

confidence: 99%

A post-processing scheme for malayalam using statistical sub-character language models

Mohan

Jawahar

2010

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

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Most of the Indian scripts do not have any robust commercial OCRs. Many of the laboratory prototypes report reasonable results at recognition/classification stage. However, word level accuracies are still poor. It is well known that word accuracy decreases as the number of characters in a word increase. For Malayalam, the average number of characters in a word is almost twice that of English. Moreover, the number of words required to cover 80% of the Malayalam language is more than forty times that of other Indian languages such as Hindi. Hence a direct dictionary based post-processing scheme is not suitable for Malayalam.In this paper, we propose a post-processing scheme which uses statistical language models at the sub-character level to boost word level recognition results. We use a multi-stage graph representation and formulate the recognition task as an optimization problem. Edges of the graph encode the language information and nodes represent the visual similarities. An optimal path from source node to destination node represents the recognized text. We validate our method on more than 10,000 words from a Malayalam corpus.

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Shape Encoded Post Processing of Gurmukhi OCR

Cited by 11 publications

References 9 publications

A Survey on Various OCR Errors

A Survey on Various OCR Errors

Error Detection in Highly Inflectional Languages

A post-processing scheme for malayalam using statistical sub-character language models

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