Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 1.1 Printing Press in Iraq and Iraqi Kurdistan 1.1 Printing Press in Iraq and Iraqi Kurdistan 1.2 Challenges in Historical Documents 1.2 Challenges in Historical Documents 1.3 Kurdish Language 1.3 Kurdish Language Related work and 2.1 Arabic/Persian
2.2 Chinese/Japanese and 2.3 Coptic
2.4 Greek
2.5 Latin
2.6 Tamizhi


Method and 3.1 Data Collection
3.2 Data Preparation and 3.3 Preprocessing
3.4 Environment Setup, 3.5 Dataset Preparation, and 3.6 Evaluation


Experiments, Results, and Discussion and 4.1 Processed Data
4.2 Dataset and 4.3 Experiments
4.4 Results and Evaluation
4.5 Discussion


Conclusion
5.1 Challenges and Limitations
Online Resources, Acknowledgments, and References Related work and 2.1 Arabic/Persian
2.2 Chinese/Japanese and 2.3 Coptic
2.4 Greek
2.5 Latin
2.6 Tamizhi Related work and 2.1 Arabic/Persian Related work and 2.1 Arabic/Persian 2.2 Chinese/Japanese and 2.3 Coptic 2.2 Chinese/Japanese and 2.3 Coptic 2.4 Greek 2.4 Greek 2.5 Latin 2.5 Latin 2.6 Tamizhi 2.6 Tamizhi Method and 3.1 Data Collection
3.2 Data Preparation and 3.3 Preprocessing
3.4 Environment Setup, 3.5 Dataset Preparation, and 3.6 Evaluation Method and 3.1 Data Collection Method and 3.1 Data Collection 3.2 Data Preparation and 3.3 Preprocessing 3.2 Data Preparation and 3.3 Preprocessing 3.4 Environment Setup, 3.5 Dataset Preparation, and 3.6 Evaluation 3.4 Environment Setup, 3.5 Dataset Preparation, and 3.6 Evaluation Experiments, Results, and Discussion and 4.1 Processed Data
4.2 Dataset and 4.3 Experiments
4.4 Results and Evaluation
4.5 Discussion Experiments, Results, and Discussion and 4.1 Processed Data Experiments, Results, and Discussion and 4.1 Processed Data 4.2 Dataset and 4.3 Experiments 4.2 Dataset and 4.3 Experiments 4.4 Results and Evaluation 4.4 Results and Evaluation 4.5 Discussion 4.5 Discussion Conclusion
5.1 Challenges and Limitations
Online Resources, Acknowledgments, and References Conclusion Conclusion 5.1 Challenges and Limitations 5.1 Challenges and Limitations Online Resources, Acknowledgments, and References Online Resources, Acknowledgments, and References 5 Conclusion The primary motivation for this study stems from the significant amounts of historical documents stored in libraries that still need to be processed. The lack of processing capabilities has led to exploring OCR technology for Kurdish, a low-resource language. Implementing OCR for extracting text from historical documents in Kurdish would greatly enhance available resources. Extensive research was conducted to assess existing OCR systems for Kurdish and other languages worldwide. The investigation focused on previous work, accuracy, and underlying technology. It was determined that Tesseract was a suitable option for this research. Once the technology was identified, efforts were made to collect digital copies of historical documents printed before 1950. This task proved challenging, as locating documents and converting them into digital format presented additional hurdles. Fortunately, the Zheen Center for Documentation and Research in Sulaymaniyah, which specializes in archiving historical documents, provided some books in the form of digital copies. Upon receiving the digitized copies, a dataset was created to train the Tesseract model. Text lines were extracted from the pages, transcribed individually, and subjected to preprocessing to prepare the dataset. With a dataset of 1233 lines, the model was trained based on the Arabic model. Following the training, the model’s performance was evaluated using various methods. Tesseract’s built-in evaluator lstmeval indicated a CER of 0.755%. Additionally, Ocreval demonstrated an average character accuracy of 84.02%. Finally, an in-house web application was developed to provide an easy-to-use interface for end-users, allowing them to interact with the model by inputting an image of a page and extracting the text. This model could be a valuable tool for libraries and centers, enabling them to extract text from historical documents and perform further processing effectively. Authors:
(1) Blnd Yaseen, University of Kurdistan Howler, Kurdistan Region - Iraq (blnd.yaseen@ukh.edu.krd);
(2) Hossein Hassani University of Kurdistan Howler Kurdistan Region - Iraq (hosseinh@ukh.edu.krd). Authors: Authors: (1) Blnd Yaseen, University of Kurdistan Howler, Kurdistan Region - Iraq (blnd.yaseen@ukh.edu.krd); (2) Hossein Hassani University of Kurdistan Howler Kurdistan Region - Iraq (hosseinh@ukh.edu.krd). This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-NODERIVS 4.0 INTERNATIONAL license. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-NODERIVS 4.0 INTERNATIONAL license. available on arxiv available on arxiv

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Training Tesseract for Low-Resource Languages

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