A Study to Recognize Printed Gujarati Characters Using Tesseract OCR

Audichya, Milind Kumar

doi:10.22214/ijraset.2017.9219

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…They utilized the existing trained data for the Gujarati script within Tesseract and aimed to shed light on its effectiveness and potential in OCR applications for the Gujarati language. (1) Jaspreet Kaur et el. conducted a study focused on recognizing typewriter-typed Hindi documents using the Tesseract OCR engine.…”

Section: Related Work Tesseract Ocrmentioning

confidence: 99%

Build a Trained Data of Tesseract OCR engine for Tifinagh Script Recognition

Benaissa,

Bahri,

El Allaoui

et al. 2023

Data and Metadata

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This article introduces a methodology for constructing a trained dataset to facilitate Tifinagh script recognition using the Tesseract OCR engine. The Tifinagh script, widely used in North Africa, poses a challenge due to the lack of built-in recognition capabilities in Tesseract. To overcome this limitation, our approach focuses on image generation, box generation, manual editing, charset extraction, and dataset compilation. By leveraging Python scripting, specialized software tools, and Tesseract's training utilities, we systematically create a comprehensive dataset for Tifinagh script recognition. The dataset enables the training and evaluation of machine learning models, leading to accurate character recognition. Experimental results demonstrate high accuracy, precision, recall, and F1 score, affirming the effectiveness of the dataset and its potential for practical applications. The results highlight the robustness of the OCR system, achieving an outstanding accuracy rate of 99.97%. The discussion underscores its superior performance in Tifinagh character recognition, exceeding the findings in the field. This methodology contributes significantly to enhancing OCR technology capabilities and encourages further research in Tifinagh script recognition, unlocking the wealth of information contained in Tifinagh documents.

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Section: Related Work Tesseract Ocrmentioning

confidence: 99%

Build a Trained Data of Tesseract OCR engine for Tifinagh Script Recognition

Benaissa,

Bahri,

El Allaoui

et al. 2023

Data and Metadata

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“…Shots of the scene are related by frame based key similarities. Some research has been done on few Indian regional language videos for information retrieval [14]. No dataset is available to work on any of the regional language videos.…”

Section: ©Ijraset (Ugc Approved Journal): All Rights Are Reservedmentioning

confidence: 99%

“…Video data normally contains audio and visual features such as color, texture, edge information, motion vectors, loudness, pitch, etc. [2,14]. In case of textual information present in video clip, the text data which are continuously being displayed for certain time gives some important information about what is currently being viewed [3,4].…”

Section: Introductionmentioning

confidence: 99%

Shot Boundary Detection for Gujarati News Video

Dave¹

2018

IJRASET

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The method to automatically detect change in shots in video scene is called video shot boundary detection. The Shot Boundary Detection(SBD) method is widely used recently in the area of video summarization, video indexing, video data mining, etc. Usually, one or more frames from each shot are selected to represent the shot information. Such frame is termed as Key Frame. This paper presents an efficient video shot boundary detection method based on visual information-based approach which use histogram difference and rank to determine shot boundary. Experimental results indicate that the proposed method achieves accountable accuracy.

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“…Gujarati is written in the Devanagari script as well and is currently supported by The Unicode Standard [6]. Gujarati is also thriving in terms of research and development in recent years [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Towards a Richer IndoWordNet with New Additions for Hindi and Gujarati Languages

Audichya¹,

Saini²,

Modh³

2022

IJACSA

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The authors of this research paper present a mechanism for dealing with loanwords, missing words, and newly developed terms inclusion issues in WordNets. WordNet has evolved as one of the most prominent Natural Language Processing (NLP) toolkits. This mechanism can be used to improve the WordNet of any language. The authors chose to work with the Hindi and Gujarati languages in this research work to achieve a higher quality research aspect because these are the languages with major dialects. The research work used more than 5000 Hindi verse-based data corpus instead of a prose-based data corpus.As a result, nearly 14000 Hindi words were discovered that were not present in the popular Hindi IndoWordNet, accounting for 13.23 percent of the total existing word count of 105000+. Working with idioms was a distinct method for the Gujarati language. Around 3500 idioms data were used, and nearly 900 Gujarati terms were discovered that did not exist in the IndoWordNet, accounting for nearly 1.4 percent of the total of 64000+ Gujarati words in the IndoWordNet. It will also contribute almost 14000 Hindi words and around 900 Gujarati words to the IndoWordNet project.

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A Study to Recognize Printed Gujarati Characters Using Tesseract OCR

Cited by 7 publications

References 6 publications

Build a Trained Data of Tesseract OCR engine for Tifinagh Script Recognition

Build a Trained Data of Tesseract OCR engine for Tifinagh Script Recognition

Shot Boundary Detection for Gujarati News Video

Towards a Richer IndoWordNet with New Additions for Hindi and Gujarati Languages

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