Challenges of Optical Character Recognition (OCR) Across Different Languages
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Optical Character Recognition (OCR) technology allows the conversion of different types of documents, including scanned paper documents, PDF files, or images captured by a digital camera into editable and searchable data. However, the task becomes challenging when dealing with various languages due to linguistic, orthographic, and structural differences.
Linguistic Challenges
1.???? Complexity of Written Scripts:
Languages vary significantly in terms of script complexity. For instance, English uses Latin-based scripts consisting of 26 letters, but an OCR designed for English may struggle to recognize Chinese scripts, which consist of thousands of unique characters. Similarly, Arabic is challenging because it is written right-to-left, and its script is characteristically cursive, with letters changing shape according to their position in a word.
?2.???? Diacritic Marks:
Many languages, like German, French and Vietnamese, use diacritical marks. These accents can decidedly change the meaning of words if omitted or misplaced, making accurate OCR even more challenging.
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Orthographic Challenges
1.???? Font Variations:
OCR technology's accuracy can diminish across different font styles, sizes, and formats, an issue not limited to a single language. A good example is the contrast between ‘handwritten’ style fonts and traditional print fonts in English. OCR technology may recognize print fonts fairly easily, but struggle with more stylized fonts.
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2.???? Spacing:
In some languages like Thai and Khmer, there are no spaces between words, making it harder for OCR technology to segment sentences into separate words.
Structural Challenges
1.???? Multi-Directional Texts:
Enabling OCR technology to cope with multiple directions is especially challenging. For example, Japanese can be written horizontally from left to right or vertically from top to bottom.
2.???? Mixed Script Documents:
Documents that have multiple languages or multiple script types pose a different challenge. For example, in bilingual or multilingual regions, a single document might contain English, Chinese, and Arabic scripts, each of which has unique considerations.
Despite continuously improving through machine learning and artificial intelligence, technology still faces some hurdles in multi-linguistic OCR.