Cursive Script Recognition Research Articles

Deep OCR for Arabic script‐based language like Pastho

AbstractDeveloping cursive script recognition systems have always been a challenging task for researchers. This article proposes a ligature‐based recognition system for the cursive Pashto script using four pre‐trained CNN models using a fine‐tuned approach. The SqueezeNet, ResNet, MobileNet and DenseNet models have been observed for the classification and the recognition of Pashto sub‐word (ligature). Overall, the proposed system is divided into two domains (Source and Target). The source domain contains the pre‐trained models used on the ImageNet Dataset. These models are later fine‐tuned using the transfer learning approach to be used for the Pashto ligature recognition. The data augmentation techniques of negative and contour are used to increase the representation of ligature images and the dataset size. The CNN models have been evaluated on the benchmarks Pashto ligatures FAST‐NU dataset. The proposed system achieved the highest recognition rate of up to 99.31% using the DenseNet architecture of Convolutional Neural Network for Pashto ligature.

Handwritten Urdu character recognition using one-dimensional BLSTM classifier

The recognition of cursive script is regarded as a subtle task in optical character recognition due to its varied representation. Every cursive script has different nature and associated challenges. As Urdu is one of cursive language that is derived from Arabic script, that is why it nearly shares the similar challenges and complexities but with more intensity. We can categorize Urdu and Arabic language on basis of its script they use. Urdu is mostly written in Nasta’liq style, whereas Arabic follows Naskh style of writing. This paper presents new and comprehensive Urdu handwritten offline database name Urdu-Nasta’liq handwritten dataset (UNHD). Currently, there is no standard and comprehensive Urdu handwritten dataset available publicly for researchers. The acquired dataset covers commonly used ligatures that were written by 500 writers with their natural handwriting on A4 size paper. UNHD is publically available and can be download form https://sites.google.com/site/researchonurdulanguage1/databases . We performed experiments using recurrent neural networks and reported a significant accuracy for handwritten Urdu character recognition.

Zoning Features and 2DLSTM for Urdu Text-line Recognition

Recognition of Urdu cursive script is a challenging task due to the implicit complexities associated with it. The performance of a recognition system is immensely dependent on extracted features. There are various features extraction approaches proposed in recent years. Among many, an approach based on zoning features proved to be efficient and popular. Such zoning features represent significant information with low complexity and high speed. In this paper, we used zoning features for the classification of Urdu Nasta’liq text lines, with a combination of 2-Dimensional Long Short Term Memory networks (2DLSTM) as learning classifier. The proposed model is evaluated on publicly available UPTI dataset and character recognition rate of 93.39% is obtained.

Robust Optical Recognition of Cursive Pashto Script Using Scale, Rotation and Location Invariant Approach.

The presence of a large number of unique shapes called ligatures in cursive languages, along with variations due to scaling, orientation and location provides one of the most challenging pattern recognition problems. Recognition of the large number of ligatures is often a complicated task in oriental languages such as Pashto, Urdu, Persian and Arabic. Research on cursive script recognition often ignores the fact that scaling, orientation, location and font variations are common in printed cursive text. Therefore, these variations are not included in image databases and in experimental evaluations. This research uncovers challenges faced by Arabic cursive script recognition in a holistic framework by considering Pashto as a test case, because Pashto language has larger alphabet set than Arabic, Persian and Urdu. A database containing 8000 images of 1000 unique ligatures having scaling, orientation and location variations is introduced. In this article, a feature space based on scale invariant feature transform (SIFT) along with a segmentation framework has been proposed for overcoming the above mentioned challenges. The experimental results show a significantly improved performance of proposed scheme over traditional feature extraction techniques such as principal component analysis (PCA).

Annotated comparisons of proposed preprocessing techniques for script recognition

Offline cursive script recognition and their associated issues are still fresh despite of last few decades' research. This paper presents an annotated comparison of proposed and recently published ...

3D stroke reconstruction and cursive script recognition with magnetometer-aided inertial measurement unit

In this paper, we present a novel stroke reconstruction algorithm for 3D cursive script using an inexpensive accelerometer and magnetometer. By imposing constraints based on ergonomics and habitual motions, the proposed algorithm can accurately estimate the orientation of a handheld device under both static and dynamic conditions. Then, the gravitational force is subtracted from the measured acceleration force for the stroke-induced acceleration, the values of which are integrated twice over time to obtain a position trajectory. The reconstruction performance is evaluated by comparing estimated stroke trajectories from a magnetometer-aided inertial measurement unit (mIMU) with those from an optical motion tracking system for alphanumeric cursive scripts. Furthermore, the recognition rates of the proposed algorithm and other conventional algorithms are compared. The experimental results reveal high accuracy in both stroke reconstruction and recognition rates.

Off-line cursive script recognition: current advances, comparisons and remaining problems

This paper presents detailed review in the field of off-line cursive script recognition. Various methods are analyzed that have been proposed to realize the core of script recognition in a word recognition system. These methods are discussed in view of the two most important properties of such systems: size and nature of the lexicon involved and whether or not a segmentation stage is present. Script recognition techniques are classified into three categories: firstly, segmentation-free methods or holistic approaches, that compare a sequence of observations derived from whole word image with similar references of words in the small lexicon. Secondly, segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word similar to human-like reading technique, in which secure features found all over the word are used to boot-strap a few candidates for a final evaluation phase; thirdly, hybrid approaches. Additionally, different feature extraction techniques are elaborated in conjunction with the classification process. In this scenario, implications of single and multiple classifiers are also observed. Finally, remaining problems are highlighted along with possible suggestion and strategies to solve them.

Performance analysis of character segmentation approach for cursive script recognition on benchmark database

This paper analyzes the improved performance of our proposed character segmentation algorithm in comparison to others presented in the literature from accuracy and computational complexity points of view. The training set is taken from IAM and test set is from CEDAR benchmark databases. Segmentation is achieved by analyzing [email protected]?s geometric features and ligatures which are strong points for segmentation in cursive handwritten words. Following pre-processing, a new heuristic technique is employed to over-segment each word at potential segmentation points. Subsequently, a simple criterion is adopted to come out with fine segmentation points based on character shape analysis. Finally, the fine segmentation points are fed to train neural network for validating segment points to enhance segmentation accuracy. Based on detailed analysis and comparison, it is observed that proposed approach enhances the cursive script segmentation accuracy with minimum computational complexity.

Combining neural gas and learning vector quantization for cursive character recognition

This paper presents a cursive character recognizer, a crucial module in any Cursive Script Recognition system based on a segmentation and recognition approach. The character classification is achieved by combining the use of neural gas (NG) and learning vector quantization (LVQ). NG is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, it is possible to find an optimal number of classes maximizing the accuracy of the LVQ classifier. A database of 58000 characters was used to train and test the models. The performance obtained is among the highest presented in the literature for the recognition of cursive characters.

Writer adaptation techniques in HMM based Off-Line Cursive Script Recognition

This work presents the application of HMM adaptation techniques to the problem of Off-Line Cursive Script Recognition. Rather than training a new model for each writer, one first creates a unique model with a mixed database and then adapts it for each different writer using his own small dataset. Experiments on a publicly available benchmark database show that an adapted system has an accuracy higher than 80% even when less than 30 word samples are used during adaptation, while a system trained using the data of the single writer only needs at least 200 words in order to achieve the same performance as the adapted models.

Exploiting zoning based on approximating splines in cursive script recognition

Because of its complexity, handwriting recognition has to exploit many sources of information to be successful, e.g. the handwriting zones. Variability of zone-lines, however, requires a more flexible representation than traditional horizontal or linear methods. The proposed method therefore employs approximating cubic splines. Using entire lines of text rather than individual words is shown to improve the zoning accuracy, especially for short words. The new method represents an improvement over existing methods in terms of range of applicability, zone-line precision and zoning-classification accuracy. Application to several problems of handwriting recognition is demonstrated and evaluated.

Cursive character recognition by learning vector quantization

This paper presents a cursive character recognizer embedded in an off-line cursive script recognition system. The recognizer is composed of two modules: the first one is a feature extractor, the second one a learning vector quantizer. The selected feature set was compared to Zernike polynomials using the same classifier. Experiments are reported on a database of about 49,000 isolated characters.

Cursive Script Recognition using Wildcards and Multiple Experts

Variability in handwriting styles suggests that many letter recognition engines cannot correctly identify some handwritten letters of poor quality at reasonable computational cost. Methods that are capable of searching the resulting sparse graph of letter candidates are therefore required. The method presented here employs `wildcards' to represent missing letter candidates. Multiple experts are used to represent different aspects of handwriting. Each expert evaluates closeness of match and indicates its confidence. Explanation experts determine the degree to which the word alternative under consideration explains extraneous letter candidates. Schemata for normalisation and combination of scores are investigated and their performance compared. Hill climbing yields near-optimal combination weights that outperform comparable methods on identical dynamic handwriting data.

Syntactic methodology of pruning large lexicons in cursive script recognition

In this paper, we present a holistic technique for pruning of large lexicons for recognition of off-line cursive script words. The technique involves extraction and representation of downward pen-strokes from the off-line cursive word to obtain a descriptor which provides a coarse characterization of word shape. Elastic matching is used to match the image descriptor with “ideal” descriptors corresponding to lexicon entries organized as a trie of stroke classes. On a set of 23,335 real cursive word images the reduction is about 70% with accuracy above 75%.

Network-based approach to online cursive script recognition

The idea of combining the network of HMMs and the dynamic programming-based search is highly relevant to online handwriting recognition. The word model of HMM network can be systematically constructed by concatenating letter and ligature HMM's while sharing common ones. Character recognition in such a network can be defined as the task of best aligning a given input sequence to the best path in the network. One distinguishing feature of the approach is that letter segmentation is obtained simultaneously with recognition but no extra computation is required.

Correction to "Ligature Modeling for Online Cursive Script Recognition"

Correction to "Ligature Modeling for Online Cursive Script Recognition"

An Off-Line Cursive Handwritten Word Recognition System and Its Application to Legal Amount Interpretation

Off-line cursive script recognition has got increasing attention during the last three decades since it is of interest in several areas such as banking and postal service. An off-line cursive handwritten word recognition system is described in this paper and is used for legal amount interpretation in personal checks. The proposed recognition system uses a set of geometric and topologic features to characterize each word. By considering the spatial distribution of these features in a word image, the proposed system maps each word into two strings of finite symbols. A local associative indexing scheme is then used on these strings to organize a vocabulary. When presented with an unknown word, the system uses the same indexing scheme to retrieve a set of candidate words likely to match the input word. A verification process is then carried out to find the best match among the candidate set. The performance of the proposed system has been tested with a legal amount image database from real bankchecks. The results obtained indicate that the proposed system is able to recognize legal amounts with great accuracy.

Ligature modeling for online cursive script recognition

Online recognition of cursive words is a difficult task owing to variable shape and ambiguous letter boundaries. The approach proposed is based on hidden Markov modeling of letters and inter-letter patterns called ligatures occurring in cursive script. For each of the letters and the ligatures we create one HMM that models temporal and spatial variability of handwriting. By networking the two kinds of HMMs, we can design a network model for all words or composite characters. The network incorporates the knowledge sources of grammatical and structural constraints so that it can better capture the characteristics of handwriting. Given the network, the problem of recognition is formulated into that of finding the most likely path from the start node to the end node. A dynamic programming-based search for the optimal input-network alignment performs character recognition and letter segmentation simultaneously and efficiently. Experiments on Korean character showed correct recognition of up to 93.3% on unconstrained samples. It has also been compared with several other schemes of HMM-based recognition to characterize the proposed approach.

Augmenting the Discrimination Power of HMM by NN for On-Line Cursive Script Recognition

For on-line handwriting recognition, a hybrid approach that combines the discrimination power of neural networks with the temporal structure of hidden Markov models is presented. Initially, all plausible letter components of an input pattern are detected by using a letter spotting technique based on hidden Markov models. A word hypothesis lattice is generated as a result of the letter spotting. All letter hypotheses in the lattice are evaluated by a neural network character recognizer in order to reinforce letter discrimination power. Then, as a new technique, an island-driven lattice search algorithm is performed to find the optimal path on the word hypothesis lattice which corresponds to the most probable word among the dictionary words. The results of this experiment suggest that the proposed framework works effectively in recognizing English cursive words. In a word recognition test, on average 88.5% word accuracy was obtained.

Implementierung von Diskreten und Semikontinuierlichen HMM in der Online-Handschrifterkennung

In this article a stochastic approach to online cursive script recognition is described. Hidden Markov Models (HMM) are trained on letter segments using discrete and semicontinous modeling for emission probability. Both variants are compared in the context of a letter based word recognizer. It is observed that in the context of the described preprocessing and feature extraction the recognition scores differ slightly with little advantage of the discrete type.