Optical Music Recognition with Convolutional Sequence-to-Sequence Models

Optical Music Recognition (OMR) is an important technology within Music Information Retrieval. Deep learning models show promising results on OMR tasks, but symbol-level annotated data sets of sufficient size to train such models are not available and difficult to develop. We present a deep learning architecture called a Convolutional Sequence-to-Sequence model to both move towards an end-to-end trainable OMR pipeline, and apply a learning process that trains on full sentences of sheet music instead of individually labeled symbols. The model is trained and evaluated on a human generated data set, with various image augmentations based on real-world scenarios. This data set is the first publicly available set in OMR research with sufficient size to train and evaluate deep learning models. With the introduced augmentations a pitch recognition accuracy of 81% and a duration accuracy of 94% is achieved, resulting in a note level accuracy of 80%. Finally, the model is compared to commercially available methods, showing a large improvements over these applications.Comment: ISMIR 201

Neural Network Approach for Character Recognition and Text Detection: A Survey

ABSTRACT Text detection and Character recognition from the image has been one of the most interesting and challenging research areas in field of pattern recognition, artificial intelligence, machine vision and image processing in the recent years. There are basically four steps that include preprocessing, feature extraction, candidate's selection, and desired character recognition to develop any of the character recognition system. Optical character recognition is the technique to convert text from the image into computer or machine readable form. Like intelligent character recognition (ICR) one character is taken at a time to make it editable by the machine. There are several approaches for developing the OCR system but in this review we emphasis on OCR using artificial neural network trained by back propagation algorithm and fuzzy logic

Efficient Handwritten Digit Classification using User-defined Classification Algorithm

In automatic numeral digit recognition system, feature selection is most important factor for achieving high recognition performance. To achieve this, the present paper proposed system for isolated handwritten numeral recognition using number of contours, skeleton features, Number of watersheds, and ratio between the numbers of foreground pixels in upper half part and lower half-part of the numerical digit image. Based on these features the present paper designed user-defined classification algorithm for handwritten digit recognition. To find the effectiveness of the proposed features, these features are given as an input for standard classification algorithms like k–nearest neighbor classifier, Support Vector Machines and other classification algorithms and evaluate the results.  The experimental result proves that the proposed features are well suited for handwritten digit recognition for both user and standard classification algorithms. The novelty of the proposed method is size invariant

Two-dimensional penalized signal regression for hand written digit recognition

Many attempts have been made to achieve successful recognition of handwritten digits. We report our results of using statistical method on handwritten digit recognition. A digitized handwritten numeral can be represented by an image with grayscales. The image includes features that are mapped into two-dimensional space with row and column coordinates. Based on this structure, two-dimensional penalized signal logistic regression (PSR) is applied to the recognition of handwritten digits. The data set is taken from the USPS zip code database that contains 7219 training images and 2007 test images. All the images have been deslanted and normalized into 16 x 16 pixels with various grayscales. The PSR method constructs a coefficient surface using a rich two-dimensional tensor product B-splines basis, so that the surface is more flexible than needed. We then penalize roughness of the coefficient surface with difference penalties on each coefficient associate with the rows and columns of the tensor product B-splines. The optimal penalty weight is found in several minutes of iterative operations. A competitive overall recognition error rate of 8.97% on the test data set was achieved. We will also review an artificial neural network approach for comparison. By using PSR, it requires neither long learning time nor large memory resources. Another advantage of the PSR method is that our results are obtained on the original USPS data set without any further image preprocessing. We also found that PSR algorithm was very capable to cope with high diversity and variation that were two major features of handwritten digits