A New Methodology for Gray-Scale Character Segmentation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Character Recognition Without Segmentation
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A System for Segmentation and Recognition of Totally Unconstrained Handwritten Numeral Strings
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Optical Character Recognition for Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Unconstrained Digit Recognition Based on Hypothesis Testing and Data Reconstruction
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Digit extraction and recognition from machine printed Gurmukhi documents
Proceedings of the International Workshop on Multilingual OCR
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A new scheme is proposed for off-line handwritten connected digit recognition, which uses a sequence of segmentation and recognition algorithms. First, the connected digits are segmented by employing both the gray scale and binary information. Then, a new set of features is extracted from the segments. The parameters of the feature set are adjusted during the training stage of the Hidden Markov Model (HMM) where the potential digits are recognized. Finally, in order to confirm the preliminary segmentation and recognition results, a recognition based segmentation method is presented.