Unconstrained Handwritten Word Recognition Research Articles

A benchmark for unconstrained online handwritten Uyghur word recognition

Despite some interesting results from different research groups, a public database for Uyghur online handwriting recognition and a baseline study are not yet available for comparison purpose. In order to fill this void, we present a database of Uyghur online handwritten words and carry out the first benchmark experiments using it. This database contains 125,020 samples of 2030 words collected from 393 writers. According to Uyghur lexicon characteristics, two out-of-vocabulary datasets are especially provided for evaluation. We carry out some unconstrained handwritten word recognition experiments on the database using recurrent neural networks as base model. Recognition results are acquired using connectionist temporal classification without lexicon search and external language model. Concatenated and averaged bidirectional recurrent layers are compared for better generalization. Based on Uyghur unicode representation, we are interested in comparing the models using different alphabets, based both on character types and character forms. To improve generalization, we propose 1D convolutional model which implements 1D convolutional layers for sequence feature extraction. In our experiments, the proposed 1D convolutional model and its variations surpassed the base recurrent layered model on the out-of-vocabulary words by clear margin. 83.23% CAR (character accurate rate) was resulted when out-of-vocabulary samples are used for testing. The highest recognition rate is as high as 94.95% CAR when the test set shares the same lexicon to the training set. The experiments in this paper can be the baseline references for the future study using this database.

An HMM framework based on spherical-linear features for online cursive handwriting recognition

In this paper a Hidden Markov Model (HMM) based writer independent online unconstrained handwritten word recognition scheme is proposed. The main steps here are segmentation of handwritten word samples into sub-strokes, feature extraction from the sub-strokes and recognition. We propose a novel but simple strategy based on the well-known discrete curve evolution for the segmentation task. Next, certain angular and linear features are extracted from the sub-strokes of word samples and are modelled as feature vectors generated from a mixture distribution. This mixture model is designed to accommodate the correlation among the angular variables. We formulate a Baum-Welch parameter estimation algorithm that can handle spherical-linear correlated data to construct an HMM. Finally, based on this HMM, we design a classifier for recognition of handwritten word samples. Simulation trials have been conducted on handwritten word sample databases of Latin and Bangla scripts demonstrating successful performance of the proposed recognition scheme.

A lexicon-driven approach for optimal segment combination in off-line recognition of unconstrained handwritten Korean words

We propose a new method for off-line recognition of unconstrained handwritten words consisting of Korean and numeric characters. To overcome the difficulty in separating touching characters, we adopt an over-segmentation strategy. Given a slice of the input word image, we find the optimal segment combination using a lexicon-driven word scoring technique and a nearest-neighbor classifier. The optimal combination gives the final segmentation positions for individual characters, along with the best matching word in the lexicon. Superiority of the proposed system has been proven by testing it with 908 images of unconstrained words handwritten on live mail pieces.

OFF-LINE UNCONSTRAINED HANDWRITTEN WORD RECOGNITION

In this paper, we describe our system for writer independent, off-line unconstrained handwritten word recognition. We have developed a new method to automatically determine the parameters of Gabor filters to extract features from slant and tilt corrected images. An algorithm is also developed to translate 2D images to 1D domain. Finally, we propose a modified dynamic programming method with fuzzy theory to recognize words. Our initial experiments have shown promising results.

Cursive word recognition using a random field based hidden Markov model

In this paper we present a two-dimensional stochastic method for the recognition of unconstrained handwritten words in a small lexicon. The method is based on an efficient combination of hidden Markov models (hmms) and causal Markov random fields (mrfs). It operates in a holistic manner, at the pixel level, on scaled binary word images which are assumed to be random field realizations. The state-related random fields act as smooth local estimators of specific writing strokes by merging conditional pixel probabilities along the columns of the image. The hmm component of our model provides an optimal switching mechanism between sets of mrf distributions in order to dynamically adapt to the features encountered during the left-to-right image scan. Experiments performed on a French omni-scriptor, omni-bank database of handwritten legal check amounts provided by the A2iA company are described in great detail.

Variable duration hidden markov model and morphological segmentation for handwritten word recognition

This paper describes a complete system for the recognition of unconstrained handwritten words using a continuous density variable duration hidden Markov model (CD-VDHMM). First, a new segmentation algorithm based on mathematical morphology is developed to translate the 2-D image into a 1-D sequence of subcharacter symbols. This sequence of symbols is modeled by the CDVDHMM. Thirty-five features are selected to represent the character symbols in the feature space. Generally, there are two information sources associated with written text; the shape information and the linguistic knowledge. While the shape information of each character symbol is modeled as a mixture Gaussian distribution, the linguistic knowledge, i.e., constraint, is modeled as a Markov chain. The variable duration state is used to take care of the segmentation ambiguity among the consecutive characters. A modified Viterbi algorithm, which provides l globally best paths, is adapted to VDHMM by incorporating the duration probabilities for the variable duration state sequence. The general string editing method is used at the postprocessing stage. The detailed experiments are carried out for two postal applications; and successful recognition results are reported.

Variable duration hidden Markov model and morphological segmentation for handwritten word recognition

This paper describes a complete system for the recognition of unconstrained handwritten words using a continuous density variable duration hidden Markov model (CD-VDHMM). First, a new segmentation algorithm based on mathematical morphology is developed to translate the 2-D image into a 1-D sequence of subcharacter symbols. This sequence of symbols is modeled by the CDVDHMM. Thirty-five features are selected to represent the character symbols in the feature space. Generally, there are two information sources associated with written text; the shape information and the linguistic knowledge. While the shape information of each character symbol is modeled as a mixture Gaussian distribution, the linguistic knowledge, i.e., constraint, is modeled as a Markov chain. The variable duration state is used to take care of the segmentation ambiguity among the consecutive characters. A modified Viterbi algorithm, which provides l globally best paths, is adapted to VDHMM by incorporating the duration probabilities for the variable duration state sequence. The general string editing method is used at the postprocessing stage. The detailed experiments are carried out for two postal applications; and successful recognition results are reported.

Off-line handwritten word recognition using a hidden Markov model type stochastic network

Because of large variations involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used in speech processing and recognition. Recently HMM has also been used with some success in recognizing handwritten words with presegmented letters. In this paper, a complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model type stochastic network is presented. Our scheme includes a morphology and heuristics based segmentation algorithm, a training algorithm that can adapt itself with the changing dictionary, and a modified Viterbi algorithm which searches for the (l+1)th globally best path based on the previous l best paths. Detailed experiments are carried out and successful recognition results are reported. >