A comparison study on optical character recognition models in mathematical equations and in any language
Optical Character Recognition[OCR] is a technology that makes use of artificial intelligence and machine learning to extract readable text from documents, images, tags or any other type of sources. It allows one to convert characters and text objects into digital data that can be easily processed, a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Control and Optimization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720725000189 |
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| author | Sofi.A. Francis M. Sangeetha |
| author_facet | Sofi.A. Francis M. Sangeetha |
| author_sort | Sofi.A. Francis |
| collection | DOAJ |
| description | Optical Character Recognition[OCR] is a technology that makes use of artificial intelligence and machine learning to extract readable text from documents, images, tags or any other type of sources. It allows one to convert characters and text objects into digital data that can be easily processed, analyzed, and modified. OCR can be applied to various types of languages in both written and spoken format. It can process everything from hand-written documents to typed-out text, making it a highly versatile technology. OCR makes use of a variety of algorithms and methods to process images, and then produces readable output, whatever language it is used for. This technology has the potential to be used for industries, banking, the medical field, security, and document storage among others. OCR faces significant challenges in accurately predicting language and mathematical expressions due to variations in handwriting styles, complex layouts, and the ambiguity of symbols. In this research, we propose assessing the results of different models that have been trained to identify an improved OCR system. The best OCR model is With the help of a decision tree model chosen. |
| format | Article |
| id | doaj-art-82a61d78f9c8485baa8dbd120ee1f6c3 |
| institution | Kabale University |
| issn | 2666-7207 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj-art-82a61d78f9c8485baa8dbd120ee1f6c32025-02-05T04:32:44ZengElsevierResults in Control and Optimization2666-72072025-03-0118100532A comparison study on optical character recognition models in mathematical equations and in any languageSofi.A. Francis0M. Sangeetha1Department of Mathematics, Dr.N.G.P Arts and Science College, Coimbatore, 641048, IndiaCorresponding author.; Department of Mathematics, Dr.N.G.P Arts and Science College, Coimbatore, 641048, IndiaOptical Character Recognition[OCR] is a technology that makes use of artificial intelligence and machine learning to extract readable text from documents, images, tags or any other type of sources. It allows one to convert characters and text objects into digital data that can be easily processed, analyzed, and modified. OCR can be applied to various types of languages in both written and spoken format. It can process everything from hand-written documents to typed-out text, making it a highly versatile technology. OCR makes use of a variety of algorithms and methods to process images, and then produces readable output, whatever language it is used for. This technology has the potential to be used for industries, banking, the medical field, security, and document storage among others. OCR faces significant challenges in accurately predicting language and mathematical expressions due to variations in handwriting styles, complex layouts, and the ambiguity of symbols. In this research, we propose assessing the results of different models that have been trained to identify an improved OCR system. The best OCR model is With the help of a decision tree model chosen.http://www.sciencedirect.com/science/article/pii/S2666720725000189Optical character recognitionArtificial intelligenceMachine learningText extractionPre-trained modelsData analysis |
| spellingShingle | Sofi.A. Francis M. Sangeetha A comparison study on optical character recognition models in mathematical equations and in any language Results in Control and Optimization Optical character recognition Artificial intelligence Machine learning Text extraction Pre-trained models Data analysis |
| title | A comparison study on optical character recognition models in mathematical equations and in any language |
| title_full | A comparison study on optical character recognition models in mathematical equations and in any language |
| title_fullStr | A comparison study on optical character recognition models in mathematical equations and in any language |
| title_full_unstemmed | A comparison study on optical character recognition models in mathematical equations and in any language |
| title_short | A comparison study on optical character recognition models in mathematical equations and in any language |
| title_sort | comparison study on optical character recognition models in mathematical equations and in any language |
| topic | Optical character recognition Artificial intelligence Machine learning Text extraction Pre-trained models Data analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2666720725000189 |
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