Indian script character recognition: a survey

Pal, Umapada; Chaudhuri, B. B.

doi:10.1016/j.patcog.2004.02.003

Cited by 456 publications

(183 citation statements)

References 88 publications

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“…Unlike printed word [22] where the row with the highest peak in horizontal projection analysis detects the Matra, it is rarely true in cursive handwritten words. The zone segmentation approach due to Roy et al [20] showed good performance in Indic scripts.…”

Section: Zone Segmentation In Indic Scriptsmentioning

confidence: 98%

“…Because of a single origin, the character names in many scripts, like Devanagari, Bangla, and Gurumukhi etc., are similar and shapes of the characters share similar appearance [22]. This can be elaborated by considering an example of the same character from three different scripts like the character 'ম'(Bengali), ਮ(Gurumukhi) and म (Devanagari).…”

Section: Similarity In Indic Scriptsmentioning

confidence: 99%

“…Devanagari and Bangla are two most popular Indic scripts where pieces of research work [7,8,13,20,22,30] exist. A few datasets are available for evaluation purpose for these two scripts.…”

Section: Dataset Collection and Scripts Used In Experimentsmentioning

confidence: 99%

“…Recognition of Indian scripts [22,38] is difficult due to their complex syntax and spatial variation of the characters when combined with other characters to form a word. Modifiers are formed when vowels are connected to the consonant and these modifiers are placed at the left, right (or both), top or bottom of the consonant.…”

Section: Introductionmentioning

confidence: 99%

“…Modifiers are formed when vowels are connected to the consonant and these modifiers are placed at the left, right (or both), top or bottom of the consonant. Presence of 'Matra' and 'modifiers' [22] makes the recognition and spotting tasks of Indian script more difficult as compared to other non-Indian scripts. Hence most of the existing word recognition works in Indic script are performed based on the segmentation of characters from words.…”

Section: Introductionmentioning

confidence: 99%

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Cross-language framework for word recognition and spotting of Indic scripts

Bhunia

Roy

Mohta

et al. 2018

Pattern Recognition

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Handwritten word recognition and spotting of low-resource scripts are difficult as sufficient training data is not available and it is often expensive for collecting data of such scripts. This paper presents a novel cross language platform for handwritten word recognition and spotting for such low-resource scripts where training is performed with a sufficiently large dataset of an available script (considered as source script) and testing is done on other scripts (considered as target script). Training with one source script and testing with another script to have a reasonable result is not easy in handwriting domain due to the complex nature of handwriting variability among scripts. Also it is difficult in mapping between source and target characters when they appear in cursive word images. The proposed Indic cross language framework exploits a large resource of dataset for training and uses it for recognizing and spotting text of other target scripts where sufficient amount of training data is not available. Since, Indic scripts are mostly written in 3 zones, namely, upper, middle and lower, we employ zone-wise character (or component) mapping for efficient learning purpose. The performance of our crosslanguage framework depends on the extent of similarity between the source and target scripts. Hence, we devise an entropy based script similarity score using source to target character mapping that will provide a feasibility of cross language transcription. We have tested our approach in three Indic scripts, namely, Bangla, Devanagari and Gurumukhi, and the corresponding results are reported.

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Section: Zone Segmentation In Indic Scriptsmentioning

confidence: 98%

Section: Similarity In Indic Scriptsmentioning

confidence: 99%

“…Devanagari and Bangla are two most popular Indic scripts where pieces of research work [7,8,13,20,22,30] exist. A few datasets are available for evaluation purpose for these two scripts.…”

Section: Dataset Collection and Scripts Used In Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-language framework for word recognition and spotting of Indic scripts

Bhunia

Roy

Mohta

et al. 2018

Pattern Recognition

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DigiNet: Prediction of Assamese handwritten digits using convolutional neural network

Dutta

Muppalaneni

2021

Concurrency and Computation

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Numerous work focusing on Indian Languages for automatically recognizing characters has been witnessed in literature in the last few decades. But it was observed that only a handful of them targeted the optical character recognition of the Assamese language despite the language being widely spoken and used in many official works and activities across North-East India. The limited contribution in this field seems to be inadequate and insufficient in their ability to recognize the handwritten characters due to dissimilarity in the people's writing style. This has motivated us to device a computational model for automatic recognition of the handwritten Assamese digits belonging to 10 different classes of 0 to 9. This study employs a convolutional neural network model (DigiNet) to learn and understand the various styles of handwritten digits. Our model exercises the convolutional neural network composed of six alternative Convolution and Pooling layers and is able to attain state-of-the-art performance on the Assamese handwritten digits, achieving a test accuracy of 93.02% which is a pretty good success. The proficiency of the model architecture is also tested on the MNIST and the Bangla handwritten numeral datasets. Furthermore, our proposed model is compared with the pre-trained VGG 19 architecture, where the performance of our model was better compared to the pre-trained model on the Assamese as well as the Bangla handwritten numerals.

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A Semi-automatic Adaptive OCR for Digital Libraries

Rawat

Kumar

Meshesha

et al. 2006

Document Analysis Systems VII

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This paper presents a novel approach for designing a semi-automatic adaptive OCR for large document image collections in digital libraries. We describe an interactive system for continuous improvement of the results of the OCR. In this paper a semi-automatic and adaptive system is implemented. Applicability of our design for the recognition of Indian Languages is demonstrated. Recognition errors are used to train the OCR again so that it adapts and learns for improving its accuracy. Limited human intervention is allowed for evaluating the output of the system and take corrective actions during the recognition process.

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Indian script character recognition: a survey

Cited by 456 publications

References 88 publications

Cross-language framework for word recognition and spotting of Indic scripts

Cross-language framework for word recognition and spotting of Indic scripts

DigiNet: Prediction of Assamese handwritten digits using convolutional neural network

A Semi-automatic Adaptive OCR for Digital Libraries

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