Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding

Retrieval of text information from natural scene images and video frames is a challenging task due to its inherent problems like complex character shapes, low resolution, background noise, etc. Available OCR systems often fail to retrieve such information in scene/video frames. Keyword spotting, an alternative way to retrieve information, performs efficient text searching in such scenarios. However, current word spotting techniques in scene/video images are script-specific and they are mainly developed for Latin script. This paper presents a novel word spotting framework using dynamic shape coding for text retrieval in natural scene image and video frames. The framework is designed to search query keyword from multiple scripts with the help of on-the-fly script-wise keyword generation for the corresponding script. We have used a two-stage word spotting approach using Hidden Markov Model (HMM) to detect the translated keyword in a given text line by identifying the script of the line. A novel unsupervised dynamic shape coding based scheme has been used to group similar shape characters to avoid confusion and to improve text alignment. Next, the hypotheses locations are verified to improve retrieval performance. To evaluate the proposed system for searching keyword from natural scene image and video frames, we have considered two popular Indic scripts such as Bangla (Bengali) and Devanagari along with English. Inspired by the zone-wise recognition approach in Indic scripts[1], zone-wise text information has been used to improve the traditional word spotting performance in Indic scripts. For our experiment, a dataset consisting of images of different scenes and video frames of English, Bangla and Devanagari scripts were considered. The results obtained showed the effectiveness of our proposed word spotting approach.Comment: Multimedia Tools and Applications, Springe

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Neoliterate adult dyslexia and literacy policies : a neurocognitive research review of a curious unexplored phenomenon

There are about 750 million adult illiterates who in principle could learn fluent reading. However, adult literacy programs have performed poorly. Various social and operational reasons may be responsible. This paper explores the role of some neurocognitive reasons in adult performance. Automatic readers of a script detect letters and words effortlessly and involuntarily. Adults learning new scripts find it hard to attain this performance. Whether illiterate or educated, adults learning a new script detect letters slowly, may make mistakes, understand little, soon abandon the task, and may also forget what they learned. When neoliterates glance at a text, they often see a jumble of letters and may process only a few of their features. They must activate reading consciously andsound out each letter. The difficulties are perceptual, and interviews suggest that perceptual distortions may continue for decades. This phenomenon called “neoliterateadult dyslexia” (NAD) has escaped attention, possibly because few educated adults need to learn new scripts, and because the adult literacy failures are often attributed to social reasons. The phenomenon also may have been missed because researchers of perceptual learning use simpler stimuli. Automaticity in reading musical notation and air traffic control may reflect similar age-related learning difficulties. In the brain, the problem may originate at the early stages of the parietal cortex at the dorsal reading path, which constricts short-term visual memory. The visual areas V1 and perhaps V4 may also be involved. Deficits affect the ventral path that provides parallel processing and direct ‘print-to-meaning’ reading. Some neuronal groups may have a sensitive period that affects the capacity to collect frequency data and to integrate the appropriate features of letters and words. Then adults do not learn to perceive letter shapes and words as easily as most children do. A lack of data and research makes it difficult to design effective interventions.The adults’ difficulties are not linguistic. Dysfluent readers simply cannot decipher the symbols in sufficient time to get to the meaning of texts, or they do so after considerable conscious visual effort. Therefore language competence seems to have little relationship to the visuospatial tasks described in this document. Language knowledge does help predict likely words when judgements must be made on the basis of just a few letter features, but the relative ease of linguistic identification may lead to reading errors. The readers’ symptoms resonate with descriptions of severe and unremitting developmental dyslexia. Certain perceptual deficits may arise during adolescence and become more severe in adulthood. Some adults may become better readers than others. But learning a script at increasingly later ages seems related to worse outcomes, though no data exist to map this trajectory. To explore this curious phenomenon, this review brings together a range of insights from of neurocognitive research, notably studies on (a) perceptual learning, including studies on feature integration and face recognition; (b) neurocognitive studies aimed at dyslexic children, (c) studies of adults suffering from brain damage that causes alexia, and (d) performance of adult literacy programs. Implications and potential remedies are also presented. The author posits the hypothesis that perhaps all people become dyslexics for new alphabets at about age 19, and thatability to read new alphabets fluently decreases with age. Neoliterate adult dyslexia (NAD) may partly account for the difficulties of adult literacy programs. Thus it seems to impact about 750 million adult illiterates. For this reason, the paper calls for urgent research into this phenomenon

Offline signature verification with user-based and global classifiers of local features

Signature verification deals with the problem of identifying forged signatures of a user from his/her genuine signatures. The difficulty lies in identifying allowed variations in a user’s signatures, in the presence of high intra-class and low interclass variability (the forgeries may be more similar to a user’s genuine signature, compared to his/her other genuine signatures). The problem can be seen as a nonrigid object matching where classes are very similar. In the field of biometrics, signature is considered a behavioral biometric and the problem possesses further difficulties compared to other modalities (e.g. fingerprints) due to the added issue of skilled forgeries. A novel offline (image-based) signature verification system is proposed in this thesis. In order to capture the signature’s stable parts and alleviate the difficulty of global matching, local features (histogram of oriented gradients, local binary patterns) are used, based on gradient information and neighboring information inside local regions. Discriminative power of extracted features is analyzed using support vector machine (SVM) classifiers and their fusion gave better results compared to state-of-the-art. Scale invariant feature transform (SIFT) matching is also used as a complementary approach. Two different approaches for classifier training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a user’s (genuine) reference signatures from other signatures. On the other hand, a single global SVM trained with difference vectors of query and reference signatures’ features of all users in the training set, learns how to weight the importance of different types of dissimilarities. The fusion of all classifiers achieves a 6.97% equal error rate in skilled forgery tests using the public GPDS-160 signature database. Former versions of the system have won several signature verification competitions such as first place in 4NSigComp2010 and 4NSigComp2012 (the task without disguised signatures); first place in 4NSigComp2011 for Chinese signatures category; first place in SigWiComp2013 for all categories. Obtained results are better than those reported in the literature. One of the major benefits of the proposed method is that user enrollment does not require skilled forgeries of the enrolling user, which is essential for real life applications