# Advancing OCR Accuracy in Image-to-LaTeX Conversion--A Critical and Creative Exploration
## 4 Preprocessing Techniques for Image Enhancement  
Preprocessing techniques for image enhancement in OCR for image-to-LaTeX conversion are crucial for improving the accuracy of the recognition process [22; 23]. These techniques address challenges related to image quality, noise, contrast, skew, and multimodal features. Recent advancements in this field have shown promising results, but critical aspects still need to be considered. One aspect is noise reduction and image denoising techniques. The noise in input images can significantly impact OCR accuracy [4; 24]. Researchers have proposed various denoising algorithms, such as median filtering, Gaussian filtering, and wavelet-based methods, to reduce noise and artifacts. Additionally, recent studies have introduced advanced denoising algorithms based on deep learning approaches, leveraging convolutional autosencoders and generative adversarial networks (GANs) [25]. These methods have demonstrated improved OCR accuracy by effectively suppressing noise patterns and preserving the legibility of characters and symbols. Another crucial preprocessing step is contrast enhancement. Enhancing image contrast can significantly improve the readability of characters and symbols, especially in low-quality or poorly illuminated images (see Figure 2). Histogram equalization techniques, such as adaptive histogram equalization (AHE) and contrast-limited adaptive histogram equalization (CLAHE), have been widely used. Recent research has explored integrating deep learning models, such as U-Net and Pix2 Pix networks, for adaptive contrast enhancement [25]. These approaches have demonstrated their effectiveness in handling varying illumination conditions and improving OCR performance. Binarization and thresholding techniques are also critical in OCR preprocessing. Binarization converts grayscale or color images into binary representations, separating foreground characters from the background [25]. Various thresholding techniques, including global thresholding, local adaptive thresholding, and hybrid methods, have been proposed to address different image characteristics and challenges. Recent advancements have introduced deep learning-based binarization methods that utilize convolutional neural networks to learn optimal thresholding strategies. These approaches have shown promising results in handling complex backgrounds and improving the segmentation of characters, leading to improved OCR accuracy. Figure 2: Visual comparison of image preprocessing techniques (noise reduction (**a**), contrast enhancement (**b**), binarization (**c**), and skew correction (**d**)) for OCR. Skew detection and correction techniques are essential for aligning images and ensuring accurate character recognition. Skewed or rotated images can negatively impact OCR accuracy. Recent research has explored the use of deep learning models, such as convolutional neural networks and recurrent neural networks, for automatic skew detection and correction [25]. These models leverage learned features and geometric transformations to estimate and rectify image skew. These techniques improve OCR accuracy by effectively aligning the images, mainly when dealing with skewed documents. Multimodal fusion and feature enhancement techniques have also gained attention in OCR preprocessing [1]. OCR models can benefit from complementary features during preprocessing by fusing multiple modalities, such as color, texture, and shape information. Recent studies have investigated the fusion of these modalities to enhance the discriminative power of OCR models. Furthermore, attention mechanisms and contextual information have been explored to guide feature enhancement [25; 26; 27]. These approaches enable OCR systems to focus on informative regions while suppressing noise or irrelevant details, ultimately improving recognition accuracy. While recent advancements in preprocessing techniques for image enhancement have shown promising results, there are still challenges to be addressed.