Recognizing handwritten Arabic words using grapheme segmentation and recurrent neural networks

The Arabic alphabet is used in around 27 languages, including Arabic, Persian, Kurdish, Urdu, and Jawi. Many researchers have developed systems for recognizing cursive handwritten Arabic words, using both holistic and segmentation-based approaches. This paper introduces a system that achieves high accuracy using efficient segmentation, feature extraction, and recurrent neural network (RNN). We describe a robust rule-based segmentation algorithm that uses special feature points identified in the word skeleton to segment the cursive words into graphemes. We show that careful selection from a wide range of features extracted during and after the segmentation stage produces a feature set that significantly reduces the label error. We demonstrate that using same RNN recognition engine, the segmentation approach with efficient feature extraction gives better results than a holistic approach that extracts features from raw pixels. We evaluated this segmentation approach against an improved version of the holistic system MDLSTM that won the ICDAR 2009 Arabic handwritten word recognition competition. On the IfN/ENIT database of handwritten Arabic words, the segmentation approach reduces the average label error by 18.5 %, the sequence error by 22.3 %, and the execution time by 31 %, relative to MDLSTM. This approach also has the best published accuracies on two IfN/ENIT test sets.     

Solving the container pre-marshalling problem using variable length genetic algorithms

In container terminals, the yard area consists of a set of blocks, which consists of a set of bays. Each bay consists of a set of stacks, which consists of a set of tiers. In the container pre-marshalling problem, an initial layout of a bay is converted to a final desired layout. The final layout follows the given loading schedule of this bay. This has a direct impact on the most important container terminal performance measure: the vessel loading time. The deviation between the current layout and the desired layout is expressed by the value of the mis-overlays. The objective of the pre-marshalling problem is to eliminate the mis-overlays with the minimum number of container movements. In this article, a variable chromosome length genetic algorithm was applied to solve the problem. The results of the new solution approach were compared against benchmark instances and the results were remarkably better.     

All-optical nanoscale pH meter

We show that an Au nanoshell with a pH-sensitive molecular adsorbate functions as a standalone, all-optical nanoscale pH meter that monitors its local environment through the pH-dependent surface-enhanced Raman scattering (SERS) spectra of the adsorbate molecules. Moreover, we also show how the performance of such a functional nanodevice can be assessed quantitatively. The complex spectral output is reduced to a simple device characteristic by application of a locally linear manifold approximation algorithm. The average accuracy of the nano-"meter" was found to be +/-0.10 pH units across its operating range.