Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline Recognition of Chinese Handwriting by Multifeature and Multilevel Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Machine Vision for Three-Dimensional Scenes
Machine Vision for Three-Dimensional Scenes
Training Invariant Support Vector Machines
Machine Learning
Recognition of Handwritten Cursive Arabic Characters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognising handwritten Arabic manuscripts using a single hidden Markov model
Pattern Recognition Letters
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Local Context in Non-Linear Deformation Models for Handwritten Character Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Kernel modified quadratic discriminant function for facial expression recognition
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
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Recognition of Tibetan wood block print is a difficult problem that has many challenging steps. We propose a two stage framework involving image preprocessing, which consists of noise removal and baseline detection, and simultaneous character segmentation and recognition by the aid of a generalized hidden Markov model (also known as gHMM). For the latter stage, we train a gHMM and run the generalized Viterbi algorithm on our image to decode observations. There are two major motivations for using gHMM. First, it incorporates a language model into our recognition system which in turn enforces grammar and disambiguates classification errors caused by printing errors and image noise. Second, gHMM solves the segmentation challenge. Simply put gHMM is an HMM where the emission model allows multiple consecutive observations to be mapped to the same state. For features of our emission model we apply line and circle Hough transform to stroke detection, and use classspecific scaling for feature weighing. With gHMM, we find KMQDF to be the most effective distance metric for discriminating character classes. The accuracy of our system is 91.29%.