Character recognition systems using Fourier transformation in incoherent light

Many attempts have been made to overcome problems involved in character recognition which have resulted in the manufacture of character reading machines. An investigation into a new approach to character recognition is described. Features for recognition are Fourier coefficients. These are generated optically by convolving characters with periodic gratings. The development of hardware to enable automatic measurement of contrast and position of periodic shadows produced by the convolution is described. Fourier coefficients of character sets were measured, many of which are tabulated. Their analysis revealed that a few low frequency sampling points could be selected to recognise sets of numerals. Limited treatment is given to show the effect of type face variations on the values of coefficients which culminated in the location of six sampling frequencies used as features to recognise numerals in two type fonts. Finally, the construction of two character recognition machines is compared and contrasted. The first is a pilot plant based on a test bed optical Fourier analyser, while the second is a more streamlined machine d(3signed for high speed reading. Reasons to indicate that the latter machine would be the most suitable to adapt for industrial and commercial applications are discussed.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.