Online handwritten script recognition
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The document proposes a system to identify six scripts including Arabic, Chinese, Cyrillic, Devnagari, Japanese, and Roman. It extracts 11 spatial and temporal features from word strokes and achieves 87.1% accuracy at the word level using 5-fold cross validation. Accuracy improves to 95% for 5-word samples and 95.5% for full text lines containing an average of 7 words. The system allows analyzing individual strokes and uses spatial and temporal information to identify scripts.
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Online handwritten script recognition
- 1. Online Handwritten Script Recognition By Dhiraj Kumar 81109134004 Guided By 1 D.Yuvaraj ( Associate prof & HOD IT)
- 2. Aim •Automatic identification of handwritten script facilitates many important applications such as automatic transcription of multilingual documents and search for documents on the Web containing a particular script. 2
- 3. Existing System The existing method deals with languages are identified • Using projection profiles of words and character shapes. • Using horizontal projection profiles and looking for the presence or absence of specific shapes in different scripts. 3
- 4. Issues in the Existing System • Existing method deals with only few characteristics • Most of the method does this in off-line 4
- 5. Proposed System • The proposed method uses the features of connected components to classify six different scripts (Arabic, Chinese, Cyrillic, Devnagari, Japanese, and Roman). • The classification is based on 11 different spatial and temporal features extracted from the strokes of the words. 5
- 6. • The proposed system attains an overall classification accuracy of 87.1 percent at the word level with 5-fold cross validation on a data set containing 13,379 words. • The classification accuracy improves to 95 percent as the number of words in the test sample is increased to five, and to 95.5 percent for complete text lines consisting of an average of seven words. 6
- 7. • . This allows us to analyze the individual strokes and use the additional temporal information for both script identification as well as text recognition. • We use stroke properties as well as the spatial and temporal information of a collection of strokes to identify the script used in the document. 7
- 8. Modules • Data collection and Preprocessing. • Line and Word Detection . • Feature Extraction. • Recognition 8
- 9. Data Collection & Preprocessing • The control for drawing that is writing the script is provided. User can choose the language and write the script document with several pages and store inside script folder 9
- 10. Line Word & Detection • High-curvature points and segmentation points: 10
- 11. System Overview Pre-processing Input (high curvature points) Dictionary Segmentation Character Recognizer Recognition Engine Context Models Word Candidates 11
- 12. Conclusion • The aim is to facilitate text recognition and to allow script-based retrieval of online handwritten documents. • The classification is done at the word level, which allows us to detect individual words of a particular script present within the text of another script. 12
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