Document Type
Conference Proceeding
Publication Date
2019
Abstract
This paper presents a comparison of offline handwriting recognition performance between two different image acquisition devices: scanner and cell-phone camera. Whereas a flat-bed scanner offers higher quality distortion free imaging, a cell-phone camera trumps on the convenience and ease of use. The aim of this research is to quantify how the extra quality obtained from a scanner impacts the offline handwriting recognition. This was evaluated with two classification framework: a segmentation-free offline Bangla handwriting transcription with sequential detection of characters/diacritics and a Bangla handwritten digit recognizer with an SVM classifier. The Boise State Bangla Handwriting dataset is used for the experiments. The highest recognition rate came when both training and testing on scanned images. The network trained with scanned images also exceeded the one trained with camera-acquired images in recognition of the camera acquired images, despite the mismatch in source.
Copyright Statement
This is an author-produced, peer-reviewed version of this conference proceeding. The final, definitive version of this document can be found online at 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), published by IEEE. Copyright restrictions may apply. https://doi.org/10.1109/ICDARW.2019.30061
Publication Information
Majid, Nishatul & Barney Smith, Elisa H. (2019). "Performance Comparison of Scanner and Camera-Acquired Data for Bangla Offline Handwriting Recognition". In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW) (pp. 31-36). IEEE. https://doi.org/10.1109/ICDARW.2019.30061