ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Recognition of Persian handwritten digits using image profiles of multiple orientations
Pattern Recognition Letters
Automatic extraction of numerical sequences in handwritten incoming mail documents
Pattern Recognition Letters
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Structural Decomposition and Statistical Description of Farsi/Arabic Handwritten Numeric Characters
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Character Recognition Systems: A Guide for Students and Practitioners
Character Recognition Systems: A Guide for Students and Practitioners
Introducing a very large dataset of handwritten Farsi digits and a study on their varieties
Pattern Recognition Letters
Arabic Handwriting Recognition Competition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Arabic handwritten digit recognition
International Journal on Document Analysis and Recognition
A blind watermarking scheme using new nontensor product wavelet filter banks
IEEE Transactions on Image Processing
Wavelet-Based Approach to Character Skeleton
IEEE Transactions on Image Processing
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In Iran like many other countries, the categorization of postal envelopes is executed manually, mostly based on the handwritten addresses and zip codes. That process is still slow and prone to man-made errors. Therefore, having an automated, accurate and efficient system to recognize handwritten zip codes is of high necessity for a faster and easier arrangement of postal envelopes, and consequently, enhanced performance of the post office. A complete system for Persian (Farsi) handwritten zip code detection and recognition is introduced in this paper. The proposed system consists of two phases; with zip code localization being the first and the main contribution of the study, and zip code digits recognition as the second. The first phase, proposes a state-of-the-art algorithm which localizes the zip code on the envelope. Subsequent to digit segmentation, handwriting zip code digits are recognized in the second phase, which includes feature extraction and classification sub-steps. The results obtained from the 50 test samples show an overall recognition rate of 92.9% in localization and recognition of handwritten zip codes.