Turkish handwritten text recognition: a case of agglutinative languages

We describe a system for recognizing unconstrained Turkish handwritten text. Turkish has agglutinative morphology and theoretically an infinite number of words that can be generated by adding more suffixes to the word. This makes lexicon-based recognition approaches, where the most likely word is selected among all the alternatives in a lexicon, unsuitable for Turkish. We describe our approach to the problem using a Turkish prefix recognizer. First results of the system demonstrates the promise of this approach, with top-10 word recognition rate of about 40% for a small test data of mixed handprint and cursive writing. The lexicon-based approach with a 17,000 word-lexicon (with test words added) achieves 56% top-10 word recognition rate

On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net

On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples

Unconstrained Scene Text and Video Text Recognition for Arabic Script

Building robust recognizers for Arabic has always been challenging. We demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes. We outperform previous state-of-the-art on two publicly available video text datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a new Arabic scene text dataset and establish baseline results. For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data. We overcome this by synthesising millions of Arabic text images from a large vocabulary of Arabic words and phrases. Our implementation is built on top of the model introduced here [37] which is proven quite effective for English scene text recognition. The model follows a segmentation-free, sequence to sequence transcription approach. The network transcribes a sequence of convolutional features from the input image to a sequence of target labels. This does away with the need for segmenting input image into constituent characters/glyphs, which is often difficult for Arabic script. Further, the ability of RNNs to model contextual dependencies yields superior recognition results.Comment: 5 page

An investigation into the use of linguistic context in cursive script recognition by computer

The automatic recognition of hand-written text has been a goal for over thirty five years. The highly ambiguous nature of cursive writing (with high variability between not only different writers, but even between different samples from the same writer), means that systems based only on visual information are prone to errors. It is suggested that the application of linguistic knowledge to the recognition task may improve recognition accuracy. If a low-level (pattern recognition based) recogniser produces a candidate lattice (i.e. a directed graph giving a number of alternatives at each word position in a sentence), then linguistic knowledge can be used to find the 'best' path through the lattice. There are many forms of linguistic knowledge that may be used to this end. This thesis looks specifically at the use of collocation as a source of linguistic knowledge. Collocation describes the statistical tendency of certain words to co-occur in a language, within a defined range. It is suggested that this tendency may be exploited to aid automatic text recognition. The construction and use of a post-processing system incorporating collocational knowledge is described, as are a number of experiments designed to test the effectiveness of collocation as an aid to text recognition. The results of these experiments suggest that collocational statistics may be a useful form of knowledge for this application and that further research may produce a system of real practical use

Beyond writing: The development of literacy in the Ancient Near East

Previous discussions of the origins of writing in the Ancient Near East have not incorporated the neuroscience of literacy, which suggests that when southern Mesopotamians wrote marks on clay in the late-fourth millennium, they inadvertently reorganized their neural activity, a factor in manipulating the writing system to reflect language, yielding literacy through a combination of neurofunctional change and increased script fidelity to language. Such a development appears to take place only with a sufficient demand for writing and reading, such as that posed by a state-level bureaucracy; the use of a material with suitable characteristics; and the production of marks that are conventionalized, handwritten, simple, and non-numerical. From the perspective of Material Engagement Theory, writing and reading represent the interactivity of bodies, materiality, and brains: movements of hands, arms, and eyes; clay and the implements used to mark it and form characters; and vision, motor planning, object recognition, and language. Literacy is a cognitive change that emerges from and depends upon the nexus of interactivity of the components

Perceptual Recognition of Arabic Literal Amounts

Since humans are the best readers, one of the most promising trends in automatic handwriting recognition is to get inspiration from psychological reading models. The underlying idea is to derive benefits from studies of human reading, in order to build efficient automatic reading systems. In this context, we propose a human reading inspired system for the recognition of Arabic handwritten literalamounts. Our approach is based on the McClelland and Rumelhart's neural model called IAM, which is one of the most referenced psychological reading models. In this article, we have adapted IAM to suit the Arabic writing characteristics, such as the natural existence of sub-words, and the particularities of the considered literal amounts vocabulary. The core of the proposed system is a neural network classifier with local knowledge representation, structured hierarchically into three levels: perceptual structural features, sub-words and words. In contrast to the classical neural networks, localist approach is more appropriate to our problem. Indeed, it introduces a priori knowledge which leads to a precise structure of the network and avoids the black box aspect as well as the learning phase. Our experimental recognition results are interesting and confirm our expectation that adapting human reading models is a promising issue in automatic handwritten word recognition

Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images

There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio

Combining diverse systems for handwritten text line recognition

In this paper, we present a recognition system for on-line handwritten texts acquired from a whiteboard. The system is based on the combination of several individual classifiers of diverse nature. Recognizers based on different architectures (hidden Markov models and bidirectional long short-term memory networks) and on different sets of features (extracted from on-line and off-line data) are used in the combination. In order to increase the diversity of the underlying classifiers and fully exploit the current state-of-the-art in cursive handwriting recognition, commercial recognition systems have been included in the combined system, leading to a final word level accuracy of 86.16%. This value is significantly higher than the performance of the best individual classifier (81.26%