OCR System Framework for MODI Scripts using Data Augmentation and Convolutional Neural Network

Joseph, Solley; Datta, Ayan; Anto, Oshin; Philip, Shynu; George, Jossy P.

doi:10.1007/978-981-15-5309-7_21

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Thus, it reports work for some subset of Modi characters having character accuracy for each character ranging from 85% to 90%. The work by Joseph et al [16] reports 91.75% accuracy using Daubechies wavelet (Db2) for Modi script character recognition. In 2021, Joseph and George [17] used CNN to recognize 46 Modi characters, which showed 99.78% accuracy.…”

Section: Modi Character Recognitionmentioning

confidence: 99%

Modi Document Transcription to Devanagari

Kundaikar,

Fadte,

Karmali

et al. 2024

ISI

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Section: Modi Character Recognitionmentioning

confidence: 99%

Modi Document Transcription to Devanagari

Kundaikar,

Fadte,

Karmali

et al. 2024

ISI

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“…Hence restoring these documents has become a complicated task. This is the inspiration for developing a system that is capable of recognizing text written in the documents using the MODI Lipi and preserving them for the future (5) .…”

Section: Rationale Of the Studymentioning

confidence: 99%

Review of Character Recognition Techniques for MODI Script

Varpe¹,

Sakhare²

2023

IJST

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Objectives:The main objective of this study is to review and compare the various methods used for Modi script recognition. Methods: The author has chosen various methods from 2010 to 2022 that are used to process MODI Script. The distinct methods employed for feature extraction and classification are compared for various datasets. A discussion on the significance of the selection of correct feature extraction and classification techniques and the comments on the methods suited to specific applications is provided. Findings: Currently, there are very few MODI translators. In contrast, millions of historical documents written in MODI remain unexplored. Novelty: The Convolutional Neural Networks (CNNs) has been used successfully for recognizing MODI Script characters. In the present study the author finds that, compared to all techniques, CNN provides maximum accuracy of 99.78%. Hence, CNN is the best character recognition technique for the MODI script.

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“…Often used techniques for data augmentation are, e.g. rotation, translation, cropping, or zooming [6].…”

Section: Related Workmentioning

confidence: 99%

3D Rendering Framework for Data Augmentation in Optical Character Recognition : (Invited Paper)

Spruck

Hawesch

Maier

et al. 2021

2021 International Symposium on Signals, Circuits and Systems (ISSCS)

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In this paper, we propose a data augmentation framework for Optical Character Recognition (OCR). The proposed framework is able to synthesize new viewing angles and illumination scenarios, effectively enriching any available OCR dataset. Its modular structure allows to be modified to match individual user requirements. The framework enables to comfortably scale the enlargement factor of the available dataset. Furthermore, the proposed method is not restricted to single frame OCR but can also be applied to video OCR. We demonstrate the performance of our framework by augmenting a 15% subset of the common Brno Mobile OCR dataset. Our proposed framework is capable of leveraging the performance of OCR applications especially for small datasets. Applying the proposed method, improvements of up to 2.79 percentage points in terms of Character Error Rate (CER), and up to 7.88 percentage points in terms of Word Error Rate (WER) are achieved on the subset. Especially the recognition of challenging text lines can be improved. The CER may be decreased by up to 14.92 percentage points and the WER by up to 18.19 percentage points for this class. Moreover, we are able to achieve smaller error rates when training on the 15% subset augmented with the proposed method than on the original nonaugmented full dataset.

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OCR System Framework for MODI Scripts using Data Augmentation and Convolutional Neural Network

Cited by 10 publications

References 9 publications

Modi Document Transcription to Devanagari

Modi Document Transcription to Devanagari

Review of Character Recognition Techniques for MODI Script

3D Rendering Framework for Data Augmentation in Optical Character Recognition : (Invited Paper)

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