# Advancing OCR Accuracy in Image-to-LaTeX Conversion--A Critical and Creative Exploration
## 11 Incorporating Active Learning Strategies for OCR Model Improvement  
The main advantage of incorporating active learning is its effectiveness in the annotation process. The old OCR training required annotating a large dataset with ground truth labels, which is expensive and time-consuming [57]. Active learning reduces these challenges by prioritizing the most informative samples for annotation [2]. Active learning reduces the annotation effort while ensuring effective model training by selectively choosing samples for which the model is uncertain or likely to make errors in. This efficient use of annotation resources saves time and reduces the costs associated with the training phase. It can also improve OCR model performance by allowing the model to learn from its mistakes and refine its understanding of symbol recognition and conversion. This iterative process allows the model to concentrate on critical areas for improvement, leading to more accurate and reliable image-to-LaTeX conversions [39]. The model becomes more robust and capable of handling various symbols, font styles, and mathematical structures encountered in real-world scenarios by actively targeting challenging samples for annotation [43]. The feedback loop created between the model and the annotation process leads to a progressive cycle of learning and refinement. As the OCR model encounters new data and challenging samples during the image-to-LaTeX conversion, it flags those samples for annotation. Experts then annotate the samples, and the newly labeled data updates the model [50]. The updated model has been equipped with additional knowledge that is then applied to the conversion process to enhance performance. This iterative cycle enables the OCR model to adapt and improve continuously. Active learning also enhances thegeneralization and adaptability of OCR models [43]. The models learn to handle various symbol variations, mathematical structures, font styles, and noise patterns by incorporating diverse samples through strategies like diversity sampling. This generalization capability enables the model to handle different handwritings, unique mathematical notations, and variations in expression formats [58]. It also fosters collaboration between the OCR system and human experts. The ability of the model to flag challenging samples for annotation enables human experts to provide their expertise and domain knowledge [59]. Experts contribute to improving the OCR model's performance by annotating these samples. This collaboration enhances the quality of the training dataset since human experts can validate and correct errors made by the model. A critical aspect to consider is selecting the active learning query strategy. Various query strategies determine how the model chooses samples for annotation. Common query strategies include query-by-committee, uncertainty sampling, and diversity sampling [60]. Uncertainty sampling selects samples with low confidence scores, indicating the model's uncertainty about their correct labels. Diversity sampling targets a diverse range of samples to ensure comprehensive training [61]. Query-by-committee includes training multiple models with slightly different initializations or architectures and selecting samples on which these models disagree, thus targeting areas of uncertainty. Deciding on an appropriate query strategy depends on the OCR system's specific requirements and the nature of the data. It is also critical to consider the balance between exploration and exploitation. Examination involves selecting samples that the model has not seen before, enabling it to learn from diverse examples, while exploitation focuses on picking pieces expected to provide the most significant improvement in the model's performance. Striking the right balance between the two ensures that the OCR model continues to learn and improve while maximizing its accuracy on challenging samples [11]. Active learning may also benefit from including ensemble techniques.