A new benchmark on the recognition of handwritten Bangla and Farsi numeral characters

  • Authors:
  • Cheng-Lin Liu;Ching Y. Suen

  • Affiliations:
  • National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, P.R. China;Computer Science Department, Center for Pattern Recognition and Machine Intelligence (CENPARMI), Concordia University, 1455 de Maisonnneuve Blvd. West, Montreal, Quebec, Canada H3G 1M8

  • Venue:
  • Pattern Recognition
  • Year:
  • 2009

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Abstract

The recognition of Indian and Arabic handwriting is drawing increasing attention in recent years. To test the promise of existing handwritten numeral recognition methods and provide new benchmarks for future research, this paper presents some results of handwritten Bangla and Farsi numeral recognition on binary and gray-scale images. For recognition on gray-scale images, we propose a process with proper image pre-processing and feature extraction. In experiments on three databases, ISI Bangla numerals, CENPARMI Farsi numerals, and IFHCDB Farsi numerals, we have achieved very high accuracies using various recognition methods. The highest test accuracies on the three databases are 99.40%, 99.16%, and 99.73%, respectively. We justified the benefit of recognition on gray-scale images against binary images, compared some implementation choices of gradient direction feature extraction, some advanced normalization and classification methods.