Comparison of Bernoulli and Gaussian HMMs using a vertical repositioning technique for off-line handwriting recognition
—In this paper a vertical repositioning method based on the center of gravity is investigated for handwriting recognition systems and evaluated on databases containing Arabic and French handwriting. Experiments show that vertical distortion in images has a large impact on the performance of HMM base...
| Authors: | , , , , , |
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| Format: | book part |
| Publication Date: | 2012 |
| Country: | España |
| Institution: | Universitat Politècnica de València (UPV) |
| Repository: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Language: | English |
| OAI Identifier: | oai:riunet.upv.es:10251/50129 |
| Online Access: | https://riunet.upv.es/handle/10251/50129 |
| Access Level: | Open access |
| Keyword: | Handwriting recognition Vertical distortion Center of gravity Recurrent neural networks Bernoulli HMMs ESTADISTICA E INVESTIGACION OPERATIVA LENGUAJES Y SISTEMAS INFORMATICOS |
| Summary: | —In this paper a vertical repositioning method based on the center of gravity is investigated for handwriting recognition systems and evaluated on databases containing Arabic and French handwriting. Experiments show that vertical distortion in images has a large impact on the performance of HMM based handwriting recognition systems. Recently good results were obtained with Bernoulli HMMs (BHMMs) using a preprocessing with vertical repositioning of binarized images. In order to isolate the effect of the preprocessing from the BHMM model, experiments were conducted with Gaussian HMMs and the LSTM-RNN tandem HMM approach with relative improvements of 33% WER on the Arabic and up to 62% on the French database. |
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