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 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.Doetsch, P.; Hamdani, M.; Ney, H.; Giménez Pastor, A.; Andrés Ferrer, J.; Juan Císcar, A. (2012). Comparison of Bernoulli and Gaussian HMMs using a vertical repositioning technique for off-line handwriting recognition. En 2012 International Conference on Frontiers in Handwriting Recognition ICFHR 2012. Institute of Electrical and Electronics Engineers (IEEE). 3-7. doi:10.1109/ICFHR.2012.194S3

Handwriting recognition by using deep learning to extract meaningful features

[EN] Recent improvements in deep learning techniques show that deep models can extract more meaningful data directly from raw signals than conventional parametrization techniques, making it possible to avoid specific feature extraction in the area of pattern recognition, especially for Computer Vision or Speech tasks. In this work, we directly use raw text line images by feeding them to Convolutional Neural Networks and deep Multilayer Perceptrons for feature extraction in a Handwriting Recognition system. The proposed recognition system, based on Hidden Markov Models that are hybridized with Neural Networks, has been tested with the IAM Database, achieving a considerable improvement.Work partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R.Pastor Pellicer, J.; Castro-Bleda, MJ.; España Boquera, S.; Zamora-Martinez, FJ. (2019). Handwriting recognition by using deep learning to extract meaningful features. 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An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition

Traditionally, the performance of ocr algorithms and systems is based on the recognition of isolated characters. When a system classifies an individual character, its output is typically a character label or a reject marker that corresponds to an unrecognized character. By comparing output labels with the correct labels, the number of correct recognition, substitution errors misrecognized characters, and rejects unrecognized characters are determined. Nowadays, although recognition of printed isolated characters is performed with high accuracy, recognition of handwritten characters still remains an open problem in the research arena. The ability to identify machine printed characters in an automated or a semi automated manner has obvious applications in numerous fields. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with theComment: 6pages, 5 figure