A Computational Theory of Contextual Knowledge in Machine Reading

Machine recognition of off–line handwriting can be achieved by either recognising words as individual symbols (word level recognition) or by segmenting a word into parts, usually letters, and classifying those parts (letter level recognition). Whichever method is used, current handwriting recognition systems cannot overcome the inherent ambiguity in writingwithout recourse to contextual information. This thesis presents a set of experiments that use Hidden Markov Models of language to resolve ambiguity in the classification process. It goes on to describe an algorithm designed to recognise a document written by a single–author and to improve recognition by adaptingto the writing style and learning new words. Learning and adaptation is achieved by reading the document over several iterations. The algorithm is designed to incorporate contextual processing, adaptation to modify the shape of known words and learning of new words within a constrained dictionary. Adaptation occurs when a word that has previously been trained in the classifier is recognised at either the word or letter level and the word image is used to modify the classifier. Learning occurs when a new word that has not been in the training set is recognised at the letter level and is subsequently added to the classifier. Words and letters are recognised using a nearest neighbour classifier and used features based on the two–dimensional Fourier transform. By incorporating a measure of confidence based on the distribution of training points around an exemplar, adaptation and learning is constrained to only occur when a word is confidently classified. The algorithm was implemented and tested with a dictionary of 1000 words. Results show that adaptation of the letter classifier improved recognition on average by 3.9% with only 1.6% at the whole word level. Two experiments were carried out to evaluate the learning in the system. It was found that learning accounted for little improvement in the classification results and also that learning new words was prone to misclassifications being propagated

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

Template Based Recognition of On-Line Handwriting

Software for recognition of handwriting has been available for several decades now and research on the subject have produced several different strategies for producing competitive recognition accuracies, especially in the case of isolated single characters. The problem of recognizing samples of handwriting with arbitrary connections between constituent characters (emph{unconstrained handwriting}) adds considerable complexity in form of the segmentation problem. In other words a recognition system, not constrained to the isolated single character case, needs to be able to recognize where in the sample one letter ends and another begins. In the research community and probably also in commercial systems the most common technique for recognizing unconstrained handwriting compromise Neural Networks for partial character matching along with Hidden Markov Modeling for combining partial results to string hypothesis. Neural Networks are often favored by the research community since the recognition functions are more or less automatically inferred from a training set of handwritten samples. From a commercial perspective a downside to this property is the lack of control, since there is no explicit information on the types of samples that can be correctly recognized by the system. In a template based system, each style of writing a particular character is explicitly modeled, and thus provides some intuition regarding the types of errors (confusions) that the system is prone to make. Most template based recognition methods today only work for the isolated single character recognition problem and extensions to unconstrained recognition is usually not straightforward. This thesis presents a step-by-step recipe for producing a template based recognition system which extends naturally to unconstrained handwriting recognition through simple graph techniques. A system based on this construction has been implemented and tested for the difficult case of unconstrained online Arabic handwriting recognition with good results

Word shape analysis for a hybrid recognition system

This paper describes two wholistic recognizers developed for use in a hybrid recognition system. The recognizers use information about the word shape. This information is strongly related to word zoning. One of the recognizers is explicitly limited by the accuracy of the zoning information extraction. The other recognizer is designed so as to avoid this limitation. The recognizers use very simple sets of features and fuzzy set based pattern matching techniques. This not only aims to increase their robustness, but also causes problems with disambiguation of the results. A verification mechanism, using letter alternatives as compound features, is introduced. Letter alternatives are obtained from a segmentation based recognizer coexisting in the hybrid system. Despite some remaining disambiguation problems, wholistic recognizers are found capable of outperforming the segmentation based recognizer. When working together in a hybrid system, the results are significantly higher than that of the individual recognizers. Recognition results are reported and compared

Analysis by synthesis in handwriting recognition

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 34-38).by Boris L. Elbert.M.S

Off-line Thai handwriting recognition in legal amount

Thai handwriting in legal amounts is a challenging problem and a new field in the area of handwriting recognition research. The focus of this thesis is to implement Thai handwriting recognition system. A preliminary data set of Thai handwriting in legal amounts is designed. The samples in the data set are characters and words of the Thai legal amounts and a set of legal amounts phrases collected from a number of native Thai volunteers. At the preprocessing and recognition process, techniques are introduced to improve the characters recognition rates. The characters are divided into two smaller subgroups by their writing levels named body and high groups. The recognition rates of both groups are increased based on their distinguished features. The writing level separation algorithms are implemented using the size and position of characters. Empirical experiments are set to test the best combination of the feature to increase the recognition rates. Traditional recognition systems are modified to give the accumulative top-3 ranked answers to cover the possible character classes. At the postprocessing process level, the lexicon matching algorithms are implemented to match the ranked characters with the legal amount words. These matched words are joined together to form possible choices of amounts. These amounts will have their syntax checked in the last stage. Several syntax violations are caused by consequence faulty character segmentation and recognition resulting from connecting or broken characters. The anomaly in handwriting caused by these characters are mainly detected by their size and shape. During the recovery process, the possible word boundary patterns can be pre-defined and used to segment the hypothesis words. These words are identified by the word recognition and the results are joined with previously matched words to form the full amounts and checked by the syntax rules again. From 154 amounts written by 10 writers, the rejection rate is 14.9 percent with the recovery processes. The recognition rate for the accepted amount is 100 percent

Recognition techniques for online Arabic handwriting recognition systems

Online recognition of Arabic handwritten text has been an on-going research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. However, different techniques have been used to build several online handwritten recognition systems for Arabic text, such as Neural Networks, Hidden Markov Model, Template Matching and others. Most of the researches on online text recognition have divided the recognition system into these three main phases which are preprocessing phase, feature extraction phase and recognition phase which considers as the most important phase and the heart of the whole system. This paper presents and compares techniques that have been used to recognize the Arabic handwriting scripts in online recognition systems. Those techniques attempt to recognize Arabic handwritten words, characters, digits or strokes. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed

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

In a split second : Handwriting pauses in typical and struggling writers

Ajuts: This research was supported by Spanish grants 2015ACUP 00175 and PID2019-108791GA-I00, awarded to NS.A two-second threshold has been typically used when analyzing the writing processes. However, there is only a weak empirical basis to claim that specific average numbers and durations of pauses may be associated with specific writing processes. We focused on handwriting execution pauses, because immature writers are known to struggle with transcription skills. We aimed to provide an evidence-based account of the average number and duration of handwriting pauses in the mid-Primary grades and to identify process-level markers of writing difficulties. Eighty 3rd and 5th graders, with and without writing difficulties, participated in the study. We examined pauses in a handwriting-only task, to be able to isolate those which could only be attributed to handwriting processes. Letter features were considered, as well as children's handwriting fluency level. The average duration of handwriting pauses was around 400ms, in line with assumptions that transcription pauses would fall under the 2,000ms threshold. We found that 3rd graders made more and longer pauses than 5th graders. Struggling writers made a similar number of pauses across grades than typically-developing children, although they were significantly longer, even after controlling for the effect of handwriting fluency. Our findings provide an evidence-based account of the duration of handwriting pauses. They also suggest that children need fewer and shorter handwriting pauses as they progress in automatizing transcription. However, some young writers struggle with letter formation even after 3 to 5 years of instruction