Enhanced OCR Techniques for Recognizing Mathematical Expressions in Scanned Documents

Yahya Baher Al-Askary; Saad Al-Momen

doi:10.30526/38.4.3640

Article Details

Enhanced OCR Techniques for Recognizing Mathematical Expressions in Scanned Documents

Authors:

Yahya Baher Al-Askary | Saad Al-Momen

Publisher:

University of Baghdad

Journal:

Ibn AL-Haitham Journal For Pure and Applied Sciences Vol.38 Issue 4, pp.295-306

Document Type:

Article

Publication Date:

10/2025

Document Language:

English

Document ID (Org No.):

2001378361

pISSN:

2521-3407

DOI:

10.30526/38.4.3640

Keywords:

Abstract:

 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.

Article Details

Enhanced OCR Techniques for Recognizing Mathematical Expressions in Scanned Documents

Authors:

Yahya Baher Al-Askary | Saad Al-Momen

Publisher:

University of Baghdad

Journal:

Ibn AL-Haitham Journal For Pure and Applied Sciences Vol.38 Issue 4, pp.295-306

Document Type:

Article

Publication Date:

10/2025

Document Language:

English

Document ID (Org No.):

2001378361

pISSN:

2521-3407

DOI:

10.30526/38.4.3640

Classification

Mathematical Expression

Neural Network

Character Recognition

Image Processing

Issues

Article Details

Enhanced OCR Techniques for Recognizing Mathematical Expressions in Scanned Documents

Authors: Yahya Baher Al-Askary | Saad Al-Momen

Publisher: University of Baghdad

Journal: Ibn AL-Haitham Journal For Pure and Applied Sciences Vol.38 Issue 4, pp.295-306

Document Type: Article

Publication Date: 10/2025

Document Language: English

Document ID (Org No.): 2001378361

pISSN: 2521-3407

DOI: 10.30526/38.4.3640

Classification

Mathematical Expression

Neural Network

Character Recognition

Image Processing

Issues

Authors:

Yahya Baher Al-Askary | Saad Al-Momen

Publisher:

University of Baghdad

Journal:

Ibn AL-Haitham Journal For Pure and Applied Sciences Vol.38 Issue 4, pp.295-306

Document Type:

Article

Publication Date:

10/2025

Document Language:

English

Document ID (Org No.):

2001378361

pISSN:

2521-3407

DOI:

10.30526/38.4.3640