Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Orientation Extraction for Wordspotting in Syriac Manuscripts
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Accurate tool based on JPEG image compression for Arabic handwritten character shape recognition
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Human reading based strategies for off-line Arabic word recognition
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Indexation of Syriac manuscripts using directional features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Style-based retrieval for ancient Syriac manuscripts
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
We describe an implemented method for the recognition of Syriac handwriting from historical manuscripts. The Syriac language has been a neglected area for handwriting recognition research, yet is interesting because the preponderance of scribe-written manuscripts offers a challenging yet tractable medium for OCR research between the extremes of typewritten text and free handwriting. Like Arabic, Syriac is written in a cursive form from right-to-left, and letter shape depends on the position within the word. The method described here does not need to find strokes or contours of the characters. Both whole words and character shapes were used in recognition experiments. After segmentation using a novel probabilistic method, features of these shapes are found that tolerate variation in formation and image quality. Each shape is recognised individually using a discriminative support vector machine with 10-fold cross-validation. We describe experiments using a variety of segmentation methods and combinations of features on characters and words. Images from scribe-written historical manuscripts are used, and the recognition results are compared with those for images taken from clearer 19th century typeset documents. Recognition rates vary from 61- 100% depending on the algorithms used and the size and source of the data set.