<b>Enhanced OCR Techniques for Recognizing Mathematical Expressions in Scanned Documents</b>

When OCR systems are utilized to recognize mathematical expressions in scanned documents, they encounter numerous challenges. These challenges arise because the mathematical expressions use an extensive array of symbols, variables, numbers, and operations, each with its distinct writing style. Moreover, mathematical expressions have a hierarchical relationship and adhere to logical rules governing the grouping of their components. This paper presents a three-step approach segmentation, symbol recognition, and interpretation to overcome these issues. The segmentation process aims to identify and separate each symbol based on its spatial location. Then, various features are extracted to describe the horizontal and vertical projections of the symbol, enabling effective recognition. Two neural network-based classification methods were proposed for the recognition of the symbols. The first one achieved 96.6% recognition rates, while the second achieved 97.7%. Lastly, the paper introduces three guidelines for interpreting the mathematical meaning of the expression and accurately converting it into textual form. The study demonstrates the potential of these methods in enhancing the capabilities of OCR systems for recognizing mathematical expressions.