Computational modelling of an optical character recognition system for Yorùbá printed text images

Oni, Olalekan Joseph; Asahiah, Franklin Oladiipo

doi:10.1016/j.sciaf.2020.e00415

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…Errors in the predicted texts are labeled in the texts as follows: (1) insertion errors: bold and underlined, (2) substitution errors: bold, (3) deletion errors: bold dash -. All plotted figures with texts generated by the sort.py tool are available on GitHub [12].…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…Character recognition has also been developed for Bangla [11], presenting unique challenges due to the variability of characters and the presence of ligatures (conjunctions of characters). Oni et al [12] developed an OCR algorithm based on generated training data. They scanned images of Yoruba texts written in Latin script and reached 3.138% character error rate using the Times New Roman font.…”

Section: Related Workmentioning

confidence: 99%

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Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

Hamplová,

Lyavdansky,

Novák

et al. 2024

CMES

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This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions, employing two state-of-the-art deep learning algorithms, namely YOLOv8 and Roboflow 3.0. The goal is to contribute to the preservation and understanding of historical texts, showcasing the potential of modern deep learning methods in archaeological research. Our research culminates in several key findings and scientific contributions. We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation-this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context. We also created and annotated an extensive dataset of Palmyrene inscriptions, a crucial resource for further research in the field. The dataset serves for training and evaluating the segmentation models. We employ comparative evaluation metrics to quantitatively assess the segmentation results, ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks. Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research. The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.

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Section: Evaluation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

Hamplová,

Lyavdansky,

Novák

et al. 2024

CMES

View full text Add to dashboard Cite

show abstract

“…As a result, the reporting network accuracy is 82.8%. Furthermore, computational modelling of an optical character recognition system for Yorùbá printed text was presented by [16]. The study observed Yorùbá language as one of those languages on the verge of extinction.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the research carried out on Yorùbá handwritten recognition focused on the classification accuracy and mostly with one algorithm for feature extraction techniques [15][16][17]. However, from literatures, a hybridized approach has proven to be more effective [18][19][20], hence this research explores the effectiveness of the hybridized feature extraction techniques over recognition accuracy when feature construction is made and feature optimization is also carried out on the classification algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Hybridized Feature Extraction Model for Offline Yorùbá Document Recognition

Ajao

Isiaka

Babatunde

2023

AJRCoS

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Document recognition is required to convert handwritten and text documents into digital equivalents, making them more easily accessible and convenient to store. This study combined feature extraction techniques for recognizing Yorùbá documents in an effort to preserve the cultural values and heritages of the Yorùbá people. Ten Yorùbá documents were acquired from Kwara State University’s Library, and ten indigenous literate writers wrote the handwritten version of the documents. These were digitized using HP Scanjet300 and pre-processed. The pre-processed image served as input to the Local Binary Pattern, Speeded-Up-Robust-Features and Histogram of Gradient. The combined extracted feature vectors were input into the Genetic Algorithm. The reduced feature vector was fed into Support Vector Machine. A 10-folds cross-validation was used to train the model: LBP-GA, SURF-GA, HOG-GA, LBP-SURF-GA, HOG-SURF-GA, LBP-HOG-GA and LBP-HOG-SURF-GA. LBP-HOG-SURF-GA for Yorùbá printed text gave 90.0% precision, 90.3% accuracy and 15.5% FPR. LBP-HOG-SURF-GA for Handwritten Yorùbá document showed 80.9% precision, 82.6% accuracy and 20.4% (FPR) LBP-HOG-SURF-GA for CEDAR gave 98.0% precision, 98.4% accuracy and 2.6% FPR. LBP-HOG-SURF-GA for MNIST gave 99% precision, 99.5% accuracy, 99.0% and 1.1% FPR. The results of the hybridized feature extractions (LBP-HOG-SURF) demonstrated that the proposed work improves significantly on the various classification metrics.

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“…However, the limitation of this work is that it only involves extracting Bangla texts from corresponding images. The paper [15] also utilized the OCR model for Standard Yorùbá printed texts by creating image text lines to be trained by Recurrent Neural Network. Although its result shows a good recognition with Times New Roman font and Ariel font image dataset, it only solved these texts and it didn't discuss the background issues.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of text extraction technique using optical character recognition and morphological reconstruction to eliminate artifacts of image’s background

Jwaid

2022

EEJET

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Text recognition of images is beneficial in a wide range of computer vision purposes such as robot navigation, document analysis, and image search. The optical character recognition (OCR) technique presents a simple tool to combine text recognition functionality to many industrial and educational applications. Best OCR results can be acquired when the background of the text image is uniform and appears as a document picture. In contrast, the challenges to recognizing accurate texts occur when the image has a non-uniform background that require further preprocessing to obtain acceptable OCR result. This work discusses three scenarios. Initially, this work will test the OCR on a normal business card as an image with a uniform background. Next, discusses the text recognition of a keypad image including digits with a non-uniform background. Here, there are two preprocessing algorithms used to enhance the OCR function to overcome the negative effect of the non-uniform background of images and to detect text with high accuracy. Finally, the developed OCR method is tested on different scanned bills and discusses the variation of the obtained results. The two algorithms are the morphological reconstruction to eliminate artifacts and create cleaner images to be further processed by OCR and the Region of Interest ROI-based OCR to spot explicit regions in a tested image. Verification for the effectiveness of the Morphological-based OCR over the ROI-based method has been conducted on a dataset of scanned electricity bills images with an accuracy of 98.2 % for Morphological-based while it is only about 89.3 % for ROI-based OCR.

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Computational modelling of an optical character recognition system for Yorùbá printed text images

Cited by 9 publications

References 6 publications

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

A Hybridized Feature Extraction Model for Offline Yorùbá Document Recognition

Development of text extraction technique using optical character recognition and morphological reconstruction to eliminate artifacts of image’s background

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