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Sinhala OCR (Optical Character Recognition)

Sinhala OCR (Optical Character Recognition) is a technology designed to convert Sinhala text from scanned images or documents into editable and searchable text. Sinhala, also known as Sinhalese, is the native language of the Sinhalese people in Sri Lanka and is written in the Sinhala script, an abugida script used for writing the Sinhala language. Sinhala OCR works by utilizing advanced algorithms and machine learning techniques to analyze the shapes and patterns of characters in the scanned images. Initially, the OCR software preprocesses the image, which involves tasks such as noise reduction, binarization, and segmentation to isolate individual characters or words. Then, the system employs pattern recognition to identify the Sinhala characters and map them to corresponding Unicode characters. The accuracy and performance of Sinhala OCR systems depend on various factors such as the quality of the input image, the complexity of the font styles used, and the language-specific challenges like ligatures and conjunct characters present in Sinhala script. To enhance accuracy, developers often train the OCR algorithms with large datasets of annotated Sinhala text samples to improve recognition rates and reduce errors. Applications of Sinhala OCR are widespread, ranging from digitizing historical documents and newspapers written in Sinhala to enabling Sinhala text search in digital archives and libraries. It also finds utility in automating data entry tasks, converting printed Sinhala text into editable digital formats, and facilitating accessibility for visually impaired individuals by converting printed materials into accessible electronic text. As technology advances, Sinhala OCR continues to evolve, incorporating advancements in artificial intelligence, deep learning, and computer vision to achieve higher accuracy rates and broader language coverage, making it an essential tool for preserving, accessing, and utilizing Sinhala language content in the digital age.