Structural Integrity of the Greek Key Motif in beta gamma-Crystallins Is Vital for Central Eye Lens Transparency

Background: We highlight an unrecognized physiological role for the Greek key motif, an evolutionarily conserved super-secondary structural topology of the beta gamma-crystallins. These proteins constitute the bulk of the human eye lens, packed at very high concentrations in a compact, globular, short-range order, generating transparency. Congenital cataract (affecting 400,000 newborns yearly worldwide), associated with 54 mutations in beta gamma-crystallins, occurs in two major phenotypes nuclear cataract, which blocks the central visual axis, hampering the development of the growing eye and demanding earliest intervention, and the milder peripheral progressive cataract where surgery can wait. In order to understand this phenotypic dichotomy at the molecular level, we have studied the structural and aggregation features of representative mutations. Methods: Wild type and several representative mutant proteins were cloned, expressed and purified and their secondary and tertiary structural details, as well as structural stability, were compared in solution, using spectroscopy. Their tendencies to aggregate in vitro and in cellulo were also compared. In addition, we analyzed their structural differences by molecular modeling in silico. Results: Based on their properties, mutants are seen to fall into two classes. Mutants A36P, L45PL54P, R140X, and G165fs display lowered solubility and structural stability, expose several buried residues to the surface, aggregate in vitro and in cellulo, and disturb/distort the Greek key motif. And they are associated with nuclear cataract. In contrast, mutants P24T and R77S, associated with peripheral cataract, behave quite similar to the wild type molecule, and do not affect the Greek key topology. Conclusion: When a mutation distorts even one of the four Greek key motifs, the protein readily self-aggregates and precipitates, consistent with the phenotype of nuclear cataract, while mutations not affecting the motif display `native state aggregation', leading to peripheral cataract, thus offering a protein structural rationale for the cataract phenotypic dichotomy ``distort motif, lose central vision''.

Online Handwritten Kannada Word Recognizer with Unrestricted Vocabulary

In this paper, we propose a novel heuristic approach to segment recognizable symbols from online Kannada word data and perform recognition of the entire word. Two different estimates of first derivative are extracted from the preprocessed stroke groups and used as features for classification. Estimate 2 proved better resulting in 88% accuracy, which is 3% more than that achieved with estimate 1. Classification is performed by statistical dynamic space warping (SDSW) classifier which uses X, Y co-ordinates and their first derivatives as features. Classifier is trained with data from 40 writers. 295 classes are handled covering Kannada aksharas, with Kannada numerals, Indo-Arabic numerals, punctuations and other special symbols like $ and #. Classification accuracies obtained are 88% at the akshara level and 80% at the word level, which shows the scope for further improvement in segmentation algorithm

Benchmarking recognition results on camera captured word image data sets

We have benchmarked the maximum obtainable recognition accuracy on five publicly available standard word image data sets using semi-automated segmentation and a commercial OCR. These images have been cropped from camera captured scene images, born digital images (BDI) and street view images. Using the Matlab based tool developed by us, we have annotated at the pixel level more than 3600 word images from the five data sets. The word images binarized by the tool, as well as by our own midline analysis and propagation of segmentation (MAPS) algorithm are recognized using the trial version of Nuance Omnipage OCR and these two results are compared with the best reported in the literature. The benchmark word recognition rates obtained on ICDAR 2003, Sign evaluation, Street view, Born-digital and ICDAR 2011 data sets are 83.9%, 89.3%, 79.6%, 88.5% and 86.7%, respectively. The results obtained from MAPS binarized word images without the use of any lexicon are 64.5% and 71.7% for ICDAR 2003 and 2011 respectively, and these values are higher than the best reported values in the literature of 61.1% and 41.2%, respectively. MAPS results of 82.8% for BDI 2011 dataset matches the performance of the state of the art method based on power law transform.

MAST: multi-script annotation toolkit for scenic text

This paper describes a semi-automatic tool for annotation of multi-script text from natural scene images. To our knowledge, this is the maiden tool that deals with multi-script text or arbitrary orientation. The procedure involves manual seed selection followed by a region growing process to segment each word present in the image. The threshold for region growing can be varied by the user so as to ensure pixel-accurate character segmentation. The text present in the image is tagged word-by-word. A virtual keyboard interface has also been designed for entering the ground truth in ten Indic scripts, besides English. The keyboard interface can easily be generated for any script, thereby expanding the scope of the toolkit. Optionally, each segmented word can further be labeled into its constituent characters/symbols. Polygonal masks are used to split or merge the segmented words into valid characters/symbols. The ground truth is represented by a pixel-level segmented image and a '.txt' file that contains information about the number of words in the image, word bounding boxes, script and ground truth Unicode. The toolkit, developed using MATLAB, can be used to generate ground truth and annotation for any generic document image. Thus, it is useful for researchers in the document image processing community for evaluating the performance of document analysis and recognition techniques. The multi-script annotation toolokit (MAST) is available for free download.

MAPS: midline analysis and propagation of segmentation

Scenic word images undergo degradations due to motion blur, uneven illumination, shadows and defocussing, which lead to difficulty in segmentation. As a result, the recognition results reported on the scenic word image datasets of ICDAR have been low. We introduce a novel technique, where we choose the middle row of the image as a sub-image and segment it first. Then, the labels from this segmented sub-image are used to propagate labels to other pixels in the image. This approach, which is unique and distinct from the existing methods, results in improved segmentation. Bayesian classification and Max-flow methods have been independently used for label propagation. This midline based approach limits the impact of degradations that happens to the image. The segmented text image is recognized using the trial version of Omnipage OCR. We have tested our method on ICDAR 2003 and ICDAR 2011 datasets. Our word recognition results of 64.5% and 71.6% are better than those of methods in the literature and also methods that competed in the Robust reading competition. Our method makes an implicit assumption that degradation is not present in the middle row.