Power-law transformation for enhanced recognition of born-digital word images

In this paper, we discuss the issues related to word recognition in born-digital word images. We introduce a novel method of power-law transformation on the word image for binarization. We show the improvement in image binarization and the consequent increase in the recognition performance of OCR engine on the word image. The optimal value of gamma for a word image is automatically chosen by our algorithm with fixed stroke width threshold. We have exhaustively experimented our algorithm by varying the gamma and stroke width threshold value. By varying the gamma value, we found that our algorithm performed better than the results reported in the literature. On the ICDAR Robust Reading Systems Challenge-1: Word Recognition Task on born digital dataset, as compared to the recognition rate of 61.5% achieved by TH-OCR after suitable pre-processing by Yang et. al. and 63.4% by ABBYY Fine Reader (used as baseline by the competition organizers without any preprocessing), we achieved 82.9% using Omnipage OCR applied on the images after being processed by our algorithm.

Language models for online handwritten Tamil word recognition

N-gram language models and lexicon-based word-recognition are popular methods in the literature to improve recognition accuracies of online and offline handwritten data. However, there are very few works that deal with application of these techniques on online Tamil handwritten data. In this paper, we explore methods of developing symbol-level language models and a lexicon from a large Tamil text corpus and their application to improving symbol and word recognition accuracies. On a test database of around 2000 words, we find that bigram language models improve symbol (3%) and word recognition (8%) accuracies and while lexicon methods offer much greater improvements (30%) in terms of word recognition, there is a large dependency on choosing the right lexicon. For comparison to lexicon and language model based methods, we have also explored re-evaluation techniques which involve the use of expert classifiers to improve symbol and word recognition accuracies.

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.

NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images

In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The word recognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.

Are Bar Associations Anticompetitive? An Empirical Analysis of Recommended Prices for Legal Services in Spain

The European Commission Report on Competition in Professional Services found that recommended prices by professional bodies have a significant negative effect on competition since they may facilitate the coordination of prices between service providers and/or mislead consumers about reasonable price levels. Professional associations argue, first, that a fee schedule may help their members to properly calculate the cost of services avoiding excessive charges and reducing consumers’ searching costs and, second, that recommended prices are very useful for cost appraisal if a litigant is condemned to pay the legal expenses of the opposing party. Thus, recommended fee schedules could be justified to some extent if they represented the cost of providing the services. We test this hypothesis using cross‐section data on a subset of recommended prices by 52 Spanish bar associations and cost data on their territorial jurisdictions. Our empirical results indicate that prices recommended by bar associations are unrelated to the cost of legal services and therefore we conclude that recommended prices have merely an anticompetitive effect.

The power of fish trader associations in the marketing of fish in Lagos State

This paper focusses on the activities of trade associations in the marketing of fish in Lagos State. The study covers 6 different markets in Lagos State of Nigeria. Analysis indicates that 86% of the traders are members of the associations. The ages of the traders range from 21 to over 55 years. However, majority are between the ages of 31 and 45 years. Traders secure their initial capital mostly from trade associations and Esusu/Ajo. Most traders have no working capital to maintain a regular series of outlets, so wholesalers turn to associations for funds, while retailers turn to wholesalers. They eventually pay back when they sell to consumers. The fish industry is found to be imperfectly competitive mostly because of the actions of fish trader associations. The fish marketing system is highly personalised and loyality exists between wholesalers and retailers and their customers

Word frequency cues word order in adults: cross-linguistic evidence

[EN] One universal feature of human languages is the division between grammatical functors and content words. From a learnability point of view, functors might provide entry points or anchors into the syntactic structure of utterances due to their high frequency. Despite its potentially universal scope, this hypothesis has not yet been tested on typologically different languages and on populations of different ages. Here we report a corpus study and an artificial grammar learning experiment testing the anchoring hypothesis in Basque, Japanese, French, and Italian adults. We show that adults are sensitive to the distribution of functors in their native language and use them when learning new linguistic material. However, compared to infants’ performance on a similar task, adults exhibit a slightly different behavior, matching the frequency distributions of their native language more closely than infants do. This finding bears on the issue of the continuity of language learning mechanism.

Detection of constitutive homomeric associations of the integrins Mac-1 subunits by fluorescence resonance energy transfer in living cells

Integrins alpha(M)beta(2) plays important role on leukocytes, such as adhesion, migration, phagocytosis, and apoptosis. It was hypothesized that homomeric associations of integrin subunits provide a driving force for integrins activation, and simultaneously inducing the formation of integrins clusters. However, experimental reports on homomeric associations between integrin subunits are still controversial. Here, we proved the homomeric associations of the isolated Mac-1 subunits in living cells using three-channel fluorescence resonance energy transfer (FRET) microscopy and FRET spectra methods. We found that the extent of homomeric associations between beta(2) subunits is higher than alpha(M) subunits. Furthermore, FRET imaging indicated that the extent of homomeric associations of the Mac-1 subunits is higher along the plasma membrane than in the cytoplasm. Finally, we suggested that homomeric associations of the transmernbrane domains or/and cytoplasmic domains may provide the driving force for the formation of constitutive homomeric associations between alpha(M) or beta(2) subunits. (c) 2006 Elsevier Inc. All rights reserved.