# Advancing OCR Accuracy in Image-to-LaTeX Conversion--A Critical and Creative Exploration
## 1 Introduction  
The digital age has transformed how we interact with written content, with optical character recognition (OCR) technology serving as a linchpin in this transformation [1]. OCR enables the conversion of printed or handwritten text into machine-readable formats, thus ushering in an era of enhanced accessibility and utility for textual data. Even though the accurate recognition and conversion of mathematical expressions into LaTeX format is still a challenge that looms large, it is within this complex and critical arena that we find the motivation and contributions of this study. The rationale for undertaking this research is underpinned by a profound recognition of the crucial role played by mathematical expression recognition within the broader OCR landscape. Mathematical notation, characterized by its intricate symbols and complex structures, has long been a veving challenge for OCR systems [2]. The precise recognition and correct conversion of mathematical expressions require understanding the symbols themselves and a deep grasp of the semantics, syntax, and intricate relationships interweaving these symbols [1]. However, prevailing OCR methods, while formidable, often fall short of capturing these subtleties, resulting in conversions that do not meet the stringent accuracy requirements [3]. The crux of the challenge liesin the visual complexity of mathematical symbols, where symbols that bear a striking resemblance can possess distinct semantic meanings [4]. The perennial noise or distortion in input images adds a layer of complexity, directly impeding the OCR system's ability to recognize and interpret mathematical expressions accurately.  
Recognizing these multifaceted challenges propels our quest for innovative solutions at the intersection of OCR and natural language processing (NLP) to enhance the accuracy of mathematical expression recognition and conversion. Hence, this extensive review study is aimed at exploring various existing natural language processing (NLP) techniques that attempt to enhance OCR accuracy in image-to-LaTeX conversions. This study also analyzes the limitations of existing approaches and recommends future directions. In turn, this study exposes existing research gaps and paves the way for innovative NLP integration techniques in OCR. In order to meet these research goals, this review study has gone through several stages (see Figure 1). These include a thorough literature review that sets out the problem statement and tries to answer the research question about how NLP techniques can be added to OCR to make it more accurate when converting images to LaTeX, an analytical screening of the techniques introduced by various research articles, and the recommendation of future directions.  
The remainder of this paper is structured as follows: the background information of this study and the related works will be stated in Section 2; the deep learning strategies for OCR in the image-to-LaTeX conversion will be stated in Section 3; the preprocessing techniques for image enhancement will be presented in Section 4; the limitations of current OCR models will be presented in Section 5; the augmenting OCR training data with LaTeX Syntax constraints will be presented in Section 6; binarization and thresholding techniques will be presented in Section 7; leveraging symbol relationships for OCR error correction will be described in Section 8; post-processing techniques for error correction in OCR for image-to-LaTeX conversion will be presented in Section 9; post-processing strategies to leverage the redundancy inherent in mathematical notation will be presented in Section 10; active learning strategies for incorporating the OCR model will be described in Section 11; evaluation metrics for OCR accuracy in image-to-LaTeX conversion will be presented in Section 12; and finally, Section 13 will conclude this study and provide recommendations for future directions.  
Figure 1: Research method.