Stroke order normalization for improving recognition of online handwritten mathematical expressions

We present a technique based on stroke order normalization for improving recognition of online handwritten mathematical expressions (ME). The stroke order dependent system has less time complexity than the stroke order free system, but it must incorporate special grammar rules to cope with stroke order variations. The stroke order normalization technique solves this problem and also the problem of unexpected stroke order variations without increasing the time complexity of ME recognition. In order to normalize stroke order, the X-Y cut method is modified since its original form causes problems when structural components in ME overlap. First, vertically ordered strokes are located by detecting vertical symbols and their upper/lower components, which are treated as MEs and reordered recursively. Second, unordered strokes on the left side of the vertical symbols are reordered as horizontally ordered strokes. Third, the remaining strokes are reordered recursively. The horizontally ordered strokes are reordered from left to right, and the vertically ordered strokes are reordered from top to bottom. Finally, the proposed stroke order normalization is combined with the stroke order dependent ME recognition system. The evaluations on the CROHME 2014 database show that the ME recognition system incorporating the stroke order normalization outperforms all other systems that use only CROHME 2014 for training while the processing time is kept low

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

Augmented incremental recognition of online handwritten mathematical expressions

This paper presents an augmented incremental recognition method for online handwritten mathematical expressions (MEs). If an ME is recognized after all strokes are written (batch recognition), the waiting time increases significantly when the ME becomes longer. On the other hand, the pure incremental recognition method recognizes an ME whenever a new single stroke is input. It shortens the waiting time but degrades the recognition rate due to the limited context. Thus, we propose an augmented incremental recognition method that not only maintains the advantage of the two methods but also reduces their weaknesses. The proposed method has two main features: one is to process the latest stroke, and the other is to find the erroneous segmentations and recognitions in the recent strokes and correct them. In the first process, the segmentation and the recognition by Cocke-Younger-Kasami (CYK) algorithm are only executed for the latest stroke. In the second process, all the previous segmentations are updated if they are significantly changed after the latest stroke is input, and then, all the symbols related to the updated segmentations are updated with their recognition scores. These changes are reflected in the CYK table. In addition, the waiting time is further reduced by employing multi-thread processes. Experiments on our dataset and the CROHME datasets show the effectiveness of this augmented incremental recognition method, which not only maintains recognition rate even compared with the batch recognition method but also reduces the waiting time to a very small level

A large vocabulary online handwriting recognition system for Turkish

Handwriting recognition in general and online handwriting recognition in particular has been an active research area for several decades. Most of the research have been focused on English and recently on other scripts like Arabic and Chinese. There is a lack of research on recognition in Turkish text and this work primarily fills that gap with a state-of-the-art recognizer for the first time. It contains design and implementation details of a complete recognition system for recognition of Turkish isolated words. Based on the Hidden Markov Models, the system comprises pre-processing, feature extraction, optical modeling and language modeling modules. It considers the recognition of unconstrained handwriting with a limited vocabulary size first and then evolves to a large vocabulary system. Turkish script has many similarities with other Latin scripts, like English, which makes it possible to adapt strategies that work for them. However, there are some other issues which are particular to Turkish that should be taken into consideration separately. Two of the challenging issues in recognition of Turkish text are determined as delayed strokes which introduce an extra source of variation in the sequence order of the handwritten input and high Out-of-Vocabulary (OOV) rate of Turkish when words are used as vocabulary units in the decoding process. This work examines the problems and alternative solutions at depth and proposes suitable solutions for Turkish script particularly. In delayed stroke handling, first a clear definition of the delayed strokes is developed and then using that definition some alternative handling methods are evaluated extensively on the UNIPEN and Turkish datasets. The best results are obtained by removing all delayed strokes, with up to 2.13% and 2.03% points recognition accuracy increases, over the respective baselines of English and Turkish. The overall system performances are assessed as 86.1% with a 1,000-word lexicon and 83.0% with a 3,500-word lexicon on the UNIPEN dataset and 91.7% on the Turkish dataset. Alternative decoding vocabularies are designed with grammatical sub-lexical units in order to solve the problem of high OOV rate. Additionally, statistical bi-gram and tri-gram language models are applied during the decoding process. The best performance, 67.9% is obtained by the large stem-ending vocabulary that is expanded with a bi-gram model on the Turkish dataset. This result is superior to the accuracy of the word-based vocabulary (63.8%) with the same coverage of 95% on the BOUN Web Corpus

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

Recognition of mathematical handwriting on whiteboards

Automatic recognition of handwritten mathematics has enjoyed significant improvements in the past decades. In particular, online recognition of mathematical formulae has seen a number of important advancements. However, in reality most mathematics is still taught and developed on regular whiteboards and offline recognition remains an open and challenging task in this area. In this thesis we develop methods to recognise mathematics from static images of handwritten expressions on whiteboards, while leveraging the strength of online recognition systems by transforming offline data into online information. Our approach is based on trajectory recovery techniques, that allow us to reconstruct the actual stroke information necessary for online recognition. To this end we develop a novel recognition process especially designed to deal with whiteboards by prudently extracting information from colour images. To evaluate our methods we use an online recogniser for the recognition task, which is specifically trained for recognition of maths symbols. We present our experiments with varying quality and sources of images. In particular, we have used our approach successfully in a set of experiments using Google Glass for capturing images from whiteboards, in which we achieve highest accuracies of 88.03% and 84.54% for segmentation and recognition of mathematical symbols respectively

Predicting and Reducing the Impact of Errors in Character-Based Text Entry

This dissertation focuses on the effect of errors in character-based text entry techniques. The effect of errors is targeted from theoretical, behavioral, and practical standpoints. This document starts with a review of the existing literature. It then presents results of a user study that investigated the effect of different error correction conditions on popular text entry performance metrics. Results showed that the way errors are handled has a significant effect on all frequently used error metrics. The outcomes also provided an understanding of how users notice and correct errors. Building on this, the dissertation then presents a new high-level and method-agnostic model for predicting the cost of error correction with a given text entry technique. Unlike the existing models, it accounts for both human and system factors and is general enough to be used with most character-based techniques. A user study verified the model through measuring the effects of a faulty keyboard on text entry performance. Subsequently, the work then explores the potential user adaptation to a gesture recognizer’s misrecognitions in two user studies. Results revealed that users gradually adapt to misrecognition errors by replacing the erroneous gestures with alternative ones, if available. Also, users adapt to a frequently misrecognized gesture faster if it occurs more frequently than the other error-prone gestures. Finally, this work presents a new hybrid approach to simulate pressure detection on standard touchscreens. The new approach combines the existing touch-point- and time-based methods. Results of two user studies showed that it can simulate pressure detection more reliably for at least two pressure levels: regular (~1 N) and extra (~3 N). Then, a new pressure-based text entry technique is presented that does not require tapping outside the virtual keyboard to reject an incorrect or unwanted prediction. Instead, the technique requires users to apply extra pressure for the tap on the next target key. The performance of the new technique was compared with the conventional technique in a user study. Results showed that for inputting short English phrases with 10% non-dictionary words, the new technique increases entry speed by 9% and decreases error rates by 25%. Also, most users (83%) favor the new technique over the conventional one. Together, the research presented in this dissertation gives more insight into on how errors affect text entry and also presents improved text entry methods