We describe an online handwriting system that is able to support 102 languages using a deep neural network architecture. This new system has completely replaced our previous segment-and-decode-based system and reduced the error rate by 20–40% relative for most languages. Further, we report new state-of-the-art results on IAM-OnDB for both the open and closed dataset setting. The system combines methods from sequence recognition with a new input encoding using Bezier curves. This leads to up to $$10\times $$ faster recognition times compared to our previous system. Through a series of experiments, we determine the optimal configuration of our models and report the results of our setup on a number of additional public datasets.

/pdf/fast-multi-language-lstm-based-online-handwriting-416h7dav06.pdf

Fast multi-language LSTM-based online handwriting recognition

Semi-supervised feature learning for improving writer identification

Personal digital bodyguards for e-security, e-learning and e-health: A prospective survey

A database of unconstrained Vietnamese online handwriting and recognition experiments by recurrent neural networks

We describe an online handwriting system that is able to support 102 languages using a deep neural network architecture. This new system has completely replaced our previous Segment-and-Decode-based system and reduced the error rate by 20%-40% relative for most languages. Further, we report new state-of-the-art results on IAM-OnDB for both the open and closed dataset setting. The system combines methods from sequence recognition with a new input encoding using Bezier curves. This leads to up to 10x faster recognition times compared to our previous system. Through a series of experiments we determine the optimal configuration of our models and report the results of our setup on a number of additional public datasets.

Fast Multi-language LSTM-based Online Handwriting Recognition

In this paper we present a new dual mode, twin-folio structured English handwriting dataset IBM_UB_1. IBM_UB_1 is our first major release from a large multilingual handwriting corpus. Containing over 6000 pages of handwritten matter, this dataset can not only be used for unconstrained handwriting recognition, more importantly, the dataset's unique twin-folio structure presents a natural fit for research on writer identification, keyword spotting, indexing and various forms of handwritten document search and retrieval. We first describe two central characteristics of the dataset - the twin-folio structure and dual modality (online/offline) - and their relevance to current research problems. Secondly, we describe the dataset, its collection and construction, and provide key descriptive statistics. Finally, we evaluate the dataset on two different research domains - handwriting recognition and writer identification - and present related experimental results.

IBM_UB_1: A Dual Mode Unconstrained English Handwriting Dataset

With the explosive growth of the tablet form factor and greater availability of pen-based direct input, online writer identification is increasingly becoming critical for person identification, digital forensics as well as downstream applications such as intelligent and adaptive user environments, search, indexing and retrieval of handwritten documents. Extant research has approached writer identification by using writing styles as a discriminative function between writers. In contrast, the authors model writing styles as a shared component of an individual's handwriting. They develop a theoretical framework for this conceptualisation and model it by using a three-level hierarchical Bayesian model (Latent Dirichlet Allocation). In this text-independent, unsupervised model each writer's handwriting is modelled as a distribution over finite writing styles that are shared among writers. They test their model on a new online handwriting dataset IBM_UB_1 and also offer benchmark comparisons by using the IAM-OnDB database. Their experiments show comparable results to the current benchmarks and demonstrate the efficacy of explicitly modelling the shared writing styles.

A hierarchical Bayesian approach to online writer identification

With the explosive growth of the tablet form factor and greater availability of pen-based direct input, writer identification in online environments is increasingly becoming critical for a variety of downstream applications such as intelligent and adaptive user environments, search, retrieval, indexing and digital forensics. Extant research has approached writer identification by using writing styles as a discriminative function between writers. In contrast, we model writing styles as a shared component of an individual&#x00E2;s handwriting. We develop a theoretical framework for this conceptualization and model this using a three level hierarchical Bayesian model (Latent Dirichlet Allocation). In this text-independent, unsupervised model each writer&#x00E2;s handwriting is modeled as a distribution over finite writing styles that are shared amongst writers. We test our model on a novel online/offline handwriting dataset IBM UB 1 which is being made available to the public. Our experiments show comparable results to current benchmarks and demonstrate the efficacy of explicitly modeling shared writing styles.

Modeling Writing Styles for Online Writer Identification: A Hierarchical Bayesian Approach

A key factor in building effective writer identification/verification systems is the amount of data required to build the underlying models. In this research we systematically examine data sufficiency bounds for two broad approaches to online writer identification--feature space models vs. writer-style space models. We report results from 40 experiments conducted on two publicly available datasets and also test identification performance for the target models using two different feature functions. Our findings show that the writer-style space model gives higher identification performance for a given level of data and further, achieves high performance levels with lesser data costs. This model appears to require as less as 20 words per page to achieve identification performance close to 80% and reaches more than 90% accuracy with higher levels of data enrollment.

Data Sufficiency for Online Writer Identification: A Comparative Study of Writer-Style Space vs. Feature Space Models

We propose a segmentation based online word recognition approach which uses a Conditional Random Field (CRF) driven beam search strategy. An efficient trie-lexicon directed, breadth-first beam search algorithm is employed in a combined segmentation-and-recognition framework to accomplish real-time recognition of online handwritten cursive English words. This framework is developed by building a candidate lattice of primitive segments obtained through over segmentation of the word pattern. The search space for the lattice is expanded by synchronously matching the lattice nodes to likely character patterns from a trie-dictionary constructed out of the target lexicon. The probable paths are evaluated by integrating character recognition scores with physical and spatial characteristics of the handwritten segments in a CRF (conditional random field) model and a beam search strategy is used to prune the set of likely paths. This approach has been benchmarked on the new IBM_UB_1 dataset as well as on the UNIPEN dataset for comparison.

https://web.tuat.ac.jp/~nakagawa/pub/2013/pdf/Shivram_ICDAR2013.pdf

Segmentation Based Online Word Recognition: A Conditional Random Field Driven Beam Search Strategy

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