# Advancing OCR Accuracy in Image-to-LaTeX Conversion--A Critical and Creative Exploration
## 7 Binarization and Thresholding Techniques  
The performance of learning models relies on large-scale annotated datasets. However, developing high-quality datasets with diverse styles and mathematical symbols poses a severe challenge due to the scarcity of datasets designed for image-to-LaTeX conversion. Scholars have tried to gather datasets representing the difficulties encountered in the conversion process [43]. The Competition on Recognition of Handwritten Mathematical Expressions (CHROME) dataset incorporates handwritten mathematical expressions that enable the evaluation and comparison of OCR models in the context of image-to-LaTeX conversion [44]. The MathML and LaTeX dataset (MALL) is also concerned with mathematical expressions expressed in MathML and LaTeX formats, enhancing the training and assessment of OCR systems for this specific task [45; 46]. These benchmark datasets create a foundation for strengthening the field by enabling standardized assessment and fostering the development of more accurate OCR models [19]. The quality and representativeness of the training data are vital since any form of bias presented in the training data may affect the accuracy of OCR systems.  
Training data mainly composed of a specific font or style makes it difficult for the OCR model to recognize symbols and characters from other styles and fonts accurately. Addressing such challenges requires keen data collection and annotation plans to ensure a balanced and diverse representation of styles, fonts, and mathematical symbols. Collaborative strategies among academic institutions and scholars can play a critical role in addressing this challenge by ensuring diversity, sharing datasets, and fostering a more comprehensive understanding of the intricacies of mathematical notation [20]. In the context of training data, the scarcity of data for rare symbols is also a critical area to be addressed. Deep learning models perform better on frequently occurring characters and styles in the training data. Scholars have examined synthetic data generation and augmentation techniques [12]. Data augmentation includes applying diverse transformations like scaling, rotation, and adding noise to augment the training data and expose the model to broad variations. Synthetic data generation is the development of artificial images with rare notations to supplement the training data. These techniques enhance OCR models' generalization capability and improve their accuracy when working with less common symbols.  
The training data's diversity and size are other essential elements that affect OCR accuracy. The learning models need large-scale training data to learn the images' underlying variations and patterns. Insufficient training data leads to overfitting, in which the model fails to generalize well to unseen examples. The diversity of the training data is also critical to ensuring generalization to different styles, fonts, and writing styles [42]. Gathering a comprehensive and diverse dataset is a non-trivial task, since it requires the consideration of variations in handwriting, mathematical domains, and notation styles. Studies should expand and diversify the available training data to improve the accuracy and reliability of OCR systems for image-to-LaTeX conversion [47]. Training data biased toward specific cultural or regional preferences lead to inaccurate OCR results. For instance, OCR models trained on datasets that mainly encompass Western mathematical notation may struggle when faced with symbols or notes used in non-Western languages and mathematical systems [31]. It is essential to incorporate diverse cultural perspectives and collaborations with experts from various regions to ensure the comprehensive coverage of mathematical notations and symbols.