Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR
Applications based on Long-Short-Term Memory (LSTM) require large amounts of data for their training. Tesseract LSTM is a popular Optical Character Recognition (OCR) engine that has been trained and used in various languages. However, its training becomes obstructed when the target language is not r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/20/9752 |
| _version_ | 1797515349575335936 |
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| author | Saman Idrees Hossein Hassani |
| author_facet | Saman Idrees Hossein Hassani |
| author_sort | Saman Idrees |
| collection | DOAJ |
| description | Applications based on Long-Short-Term Memory (LSTM) require large amounts of data for their training. Tesseract LSTM is a popular Optical Character Recognition (OCR) engine that has been trained and used in various languages. However, its training becomes obstructed when the target language is not resourceful. This research suggests a remedy for the problem of scant data in training Tesseract LSTM for a new language by exploiting a training dataset for a language with a similar script. The target of the experiment is Kurdish. It is a multi-dialect language and is considered less-resourced. We choose Sorani, one of the Kurdish dialects, that is mostly written in Persian-Arabic script. We train Tesseract using an Arabic dataset, and then we use a considerably small amount of texts in Persian-Arabic to train the engine to recognize Sorani texts. Our dataset is based on a series of court case documents in the Kurdistan Region of Iraq. We also fine-tune the engine using 10 Unikurd fonts. We use Lstmeval and Ocreval to evaluate the outputs. The result indicates the achievement of 95.45% accuracy. We also test the engine using texts outside the context of court cases. The accuracy of the system remains close to what was found earlier indicating that the script similarity could be used to overcome the lack of large-scale data. |
| first_indexed | 2024-03-10T06:44:15Z |
| format | Article |
| id | doaj.art-5077598697104fcc9ee94afd9f2804e2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:44:15Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5077598697104fcc9ee94afd9f2804e22023-11-22T17:23:52ZengMDPI AGApplied Sciences2076-34172021-10-011120975210.3390/app11209752Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCRSaman Idrees0Hossein Hassani1Department of Computer Science and Engineering, University of Kurdistan Hewlêr, 30 Meter, Kurdistan Region, Erbil 44001, IraqDepartment of Computer Science and Engineering, University of Kurdistan Hewlêr, 30 Meter, Kurdistan Region, Erbil 44001, IraqApplications based on Long-Short-Term Memory (LSTM) require large amounts of data for their training. Tesseract LSTM is a popular Optical Character Recognition (OCR) engine that has been trained and used in various languages. However, its training becomes obstructed when the target language is not resourceful. This research suggests a remedy for the problem of scant data in training Tesseract LSTM for a new language by exploiting a training dataset for a language with a similar script. The target of the experiment is Kurdish. It is a multi-dialect language and is considered less-resourced. We choose Sorani, one of the Kurdish dialects, that is mostly written in Persian-Arabic script. We train Tesseract using an Arabic dataset, and then we use a considerably small amount of texts in Persian-Arabic to train the engine to recognize Sorani texts. Our dataset is based on a series of court case documents in the Kurdistan Region of Iraq. We also fine-tune the engine using 10 Unikurd fonts. We use Lstmeval and Ocreval to evaluate the outputs. The result indicates the achievement of 95.45% accuracy. We also test the engine using texts outside the context of court cases. The accuracy of the system remains close to what was found earlier indicating that the script similarity could be used to overcome the lack of large-scale data.https://www.mdpi.com/2076-3417/11/20/9752optical character recognitiontesseractprinted-document OCRKurdish-OCR systemoffline character recognition system |
| spellingShingle | Saman Idrees Hossein Hassani Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR Applied Sciences optical character recognition tesseract printed-document OCR Kurdish-OCR system offline character recognition system |
| title | Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR |
| title_full | Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR |
| title_fullStr | Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR |
| title_full_unstemmed | Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR |
| title_short | Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR |
| title_sort | exploiting script similarities to compensate for the large amount of data in training tesseract lstm towards kurdish ocr |
| topic | optical character recognition tesseract printed-document OCR Kurdish-OCR system offline character recognition system |
| url | https://www.mdpi.com/2076-3417/11/20/9752 |
| work_keys_str_mv | AT samanidrees exploitingscriptsimilaritiestocompensateforthelargeamountofdataintrainingtesseractlstmtowardskurdishocr AT hosseinhassani exploitingscriptsimilaritiestocompensateforthelargeamountofdataintrainingtesseractlstmtowardskurdishocr |