A complete character recognition and transliteration technique for Devanagari script
This addresses the need for accurate script conversion in natural language processing for Devanagari users, but it appears incremental as it builds on existing segmentation and recognition methods.
The paper tackles the problem of automatic transliteration of Devanagari script to Roman alphabets by developing a technique that includes segmentation for overlapping lines and conjuncts, and uses statistical and structural features for character recognition. The result is a mapping that ensures phonetic similarity between the scripts, though no concrete performance numbers are provided.
Transliteration involves transformation of one script to another based on phonetic similarities between the characters of two distinctive scripts. In this paper, we present a novel technique for automatic transliteration of Devanagari script using character recognition. One of the first tasks performed to isolate the constituent characters is segmentation. Line segmentation methodology in this manuscript discusses the case of overlapping lines. Character segmentation algorithm is designed to segment conjuncts and separate shadow characters. Presented shadow character segmentation scheme employs connected component method to isolate the character, keeping the constituent characters intact. Statistical features namely different order moments like area, variance, skewness and kurtosis along with structural features of characters are employed in two phase recognition process. After recognition, constituent Devanagari characters are mapped to corresponding roman alphabets in way that resulting roman alphabets have similar pronunciation to source characters.