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The research community considers handwritten word recognition (HWR) as an open research problem to date. The reasons behind this are variations in intra-/interpersonal writing style, overlapping and/or touching characters in a word, degraded scanned document images, etc. Two major approaches, namely holistic and analytical, are followed by the researchers while designing an HWR system. In this work, we have followed the holistic approach as it works well on limited and pre-defined lexicon as compared to the analytical approach. As observed in the literature related to handwritten word recognition, irrespective of the approaches, researchers generally extract various local features from hypothetically partitioned segments of a word image while dealing with the said problem. However, no such work has been found which has considered inter-segment similarity that might carry some distinct information about different patterns (here, word segments). To this end, in the present work, we have used Hausdorff and Fréchet distances to quantize the similarity among all possible word segments taking two at a time. Along with this, conventional chain code histogram (a shape-based feature descriptor) and modified negative refraction-based shape transformation features have been used. Finally, a majority voting schema is used to combine outputs from six different classifiers. The model has been evaluated on two standard databases, namely IAM and IFN/ENIT, and the results obtained are promising in comparison with state-of-the-art holistic word recognition methods. Moreover, a performance comparison of the present method with some deep learning models confirms the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

In ancient times, there was no system to record or document music. A basic notation system to write European music was formulated around 14th century in the Baroque period which slowly evolved into the standard notation system that we have today. Later, the musical pieces from the classical and post-classical period of European music were documented as scores using this standard European staff notations. These notations are used by most of the modern genres of music due to their versatility. Hence, it is very important to develop a method that can store such music sheets containing handwritten music scores digitally. Optical music recognition (OMR) is a system that automatically interprets the scanned handwritten music scores. In this work, we have proposed a classifier ensemble of deep transfer learning models with support vector machine (SVM) as the aggregator for handwritten music symbol recognition. We have applied three pre-trained deep learning models, namely ResNet50, GoogleNet and DenseNet161 (each trained on ImageNet), and fine-tuned on our target datasets i.e., music symbol image datasets. The proposed ensemble technique can capture a more complex association of the base classifiers, thus improving the overall performance. We have evaluated the proposed model on five publicly available standard datasets, namely Handwritten Online Music Symbols (HOMUS), Capitan_Score_Uniform, Capitan_Score_Non-uniform, Rebelo_real and Fornés, and achieved state-of-the-art results for all these datasets. Additionally, we have evaluated our model on publicly available two non-music symbols datasets, namely CMATERdb 2.1.2 containing 120 handwritten Bangla city names and CMATERdb 3.1.1 dataset containing handwritten Bangla numerals to validate its effectiveness on diversified datasets. The source code of this present work is available at https://github.com/ashis0013/Music-Symbol-Recognition. [ABSTRACT FROM AUTHOR]

Word spotting in handwritten document images is a field of immense interest due to its widespread applications. Recognition-free and recognition-based approaches are the two comprehensively studied regimes for the said problem out of which the first one is more realistic for practical applications. In literature, several works have been found that have used contour and distance-based measures for matching of the profiles of two word images. Although this is a prudent choice for printed words, the same often faces bottlenecks for unconstrained handwriting. To this end, this work applies dynamic time warping algorithm on logarithmic profiles of handwritten word images to lessen the uncontrolled profile variation that occurs due to elongation while writing some characters. We have considered both global and local interpretations of a word image by dividing it vertically into a number of sub-parts. This multi-view analysis provides close-up views of different approximations for the same word image. Finally, a voting scheme is evoked to produce the final decision. Besides, we have adopted a pruning method to pre-filter the target word images prior to applying the voting-based word matching scheme. The method has been tested on word images, taken from Qatar University Writer Identification database. We have obtained satisfactory results as compared to many state-of-the-art methods that also include deep learning-based feature extraction models. [ABSTRACT FROM AUTHOR]

With a wide variety of forms being generated in different organizations daily, efficient and quick retrieval of information from these forms becomes a pressing need. The data on these forms are imperative to any commercial or professional purpose and thus, efficient retrieval of this data is important for further processing of the same. An automatic form processing system retrieves the content of a filled-in form image for useful storage of the same. Despite a large population of the world speaking in Bangla, to the best of our knowledge, there is no significant research work found in literature which deals with form data written in Bangla. To bridge this research gap, in the present scope of the work, we have developed a system that addresses four important aspects of processing of form data written using Bangla script. Our work has primarily been divided into four major modules: touching component separation, text non-text separation, handwritten printed text separation and alphabet numeral separation. The vital problem of touching component separation has been addressed using a novel rule-based method. For text non-text separation, handwritten printed text separation and alphabet numeral separation, we have used a machine learning based approach using feature engineering where the model for each case has been finalized after exhaustive experiments. Further, in each of the last three modules, we have applied some new features along with some existing features to appropriately tune the modules to obtain optimum results. Notably, we have also prepared a self-made database of filled-in forms. To create different training models, first the filled-in form images are binarized, and then different types of components are colored uniquely to obtain images which act as the ground truth for our reference. Evaluation of modules on the said database produces reasonably satisfactory results considering the complexity of the research problem. The code along with some filled-in sample form images and their respective ground truth images are provided in the link https://github.com/rajdeep-cse17/Form_Processing. [ABSTRACT FROM AUTHOR]

Recognition of unconstrained handwritten word images is an interesting research problem which gets more challenging when lexicon-free words are considered. Prerequisite for developing a lexicon-free handwritten word recognition technique is the segmentation of a word image into its constituent character set. Therefore, a competent character segmentation technique is required to design a comprehensive word recognition module. However, the literature study reveals that there is no standard word image database with ground truth information. As a result, most character segmentation algorithms found in the literature rely on self-made databases with manual evaluation. To fill the research need, in the present scope of the work, a comprehensive database consisting of handwritten Bangla word images is prepared primarily for evaluating any character segmentation algorithms. Additionally, the present work also provides two types of ground truth images related to segmented character shapes of the word images. Besides, an evaluation tool is developed for assessing the performance of any character segmentation algorithm on the developed benchmark database. The benchmark result, as found here, is 0.9212 (F-score) which outperforms some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Handwritten music symbol recognition is considered by the research fraternity as a critical research problem. It becomes more critical when the symbols are collected from handwritten music sheets in offline mode. Most of the research findings, available in the literature, have tried to recognize the said symbols using various shape based features. But this approach limits system performance when we dealt with lookalike symbols such as half note, eight note and quarter note. To encounter this, in the present work we have used a texture based feature descriptor, called Daisy, for the said purpose. Though Daisy descriptor yields reasonably good recognition accuracy, but it generates a high dimensional feature vector. Hence, in this work, Quantum concept inspired Grey Wolf Optimization, named as QGWO, has been applied to select optimal feature subset from this high dimensional feature vector. We have applied the proposed method on six different standard music symbol datasets that include HOMUS, Capitan_score_uniform, Capitan_score_non-uniform, Fornés, Rebelo_real and Rebelo_synthetic datasets. On these datasets we have achieved recognition accuracies 93.07%, 99.22%, 99.20%, 99.49% and 100.00% respectively with 39.63%, 49.75%, 42.50%, 67.62%, 54.37% and 71.25% of actual feature dimension (i.e., 800) respectively. Additionally, we have compared our results with some state-of-the-art methods along with two recent deep learning based models, and it has been found that the present approach outperforms those. [ABSTRACT FROM AUTHOR]

Handwritten word recognition, a classical pattern recognition problem, converts a word image into its machine editable form. Mainly two basic approaches are followed to solve this problem, one is segmentation-based and the other is holistic. A number of research attempts have shown that the holistic approach performs better than its counterpart when the lexicon is predefined, fixed and small in size. Relying on this, initial benchmark recognition accuracy on CMATERdb2.1.2, a publicly available database consists of handwritten city names in Bangla, was reported following a holistic word recognition protocol. In the present work, we have followed the same trend to recognize the word samples of the said database and set a new benchmark recognition accuracy. A sparse convolutional neural network (CNN)-based model which is a low-cost trainable model has been developed for this. We have relied on a recently proposed hypothesis, known as lottery ticket hypothesis for pruning the layers of CNN model methodically, and derived a low-resource model having much less number of training parameters. This model competently surpasses the previously reported recognition accuracy on the said database by a significant margin with an axed training cost. [ABSTRACT FROM AUTHOR]

Feature selection plays a key role in reducing the dimensionality of a feature vector by discarding redundant and irrelevant ones. In this paper, a Genetic Algorithm-based hierarchical feature selection (HFS) model has been designed to optimize the local and global features extracted from each of the handwritten word images under consideration. In this context, two recently developed feature descriptors based on shape and texture of the word images have been taken into account. Experimentation is conducted on an in-house dataset of 12,000 handwritten word samples written in Bangla script. This database comprises names of 80 popular cities of West Bengal, a state of India. Proposed model not only reduces the feature dimension by nearly 28%, but also enhances the performance of the handwritten word recognition (HWR) technique by 1.28% over the recognition performance obtained with unreduced feature set. Moreover, the proposed HFS-based HWR system performs better in comparison with some recently developed methods on the present dataset. [ABSTRACT FROM AUTHOR]

Handwritten words can never complement printed words because the former are mostly written in either skewed or slanted form or in both. This very nature of handwriting adds a huge overhead when converting word images into machine-editable format through an optical character recognition system. Therefore, slope and slant corrections are considered as the fundamental pre-processing tasks in handwritten word recognition. For solving this, researchers have followed a two-pass approach where the slope of the word is corrected first and then slant correction is carried out subsequently, thus making the system computationally expensive. To address this issue, we propose a novel one-pass method, based on fitting an oblique ellipse over the word images, to estimate both the slope and slant angles of the same. Furthermore, we have developed three databases considering word images of three popular scripts used in India, namely Bangla, Devanagari, and Roman, along with ground truth information. The experimental results revealed the effectiveness of the proposed method over some state-of-the-art methods used for the aforementioned problem. [ABSTRACT FROM AUTHOR]

Word searching or keyword spotting is an important research problem in the domain of document image processing. The solution to the said problem for handwritten documents is more challenging than for printed ones. In this work, a two-stage word searching schema is introduced. In the first stage, all the irrelevant words with respect to a search word are filtered out from the document page image. This is carried out using a zonal feature vector, called pre-selection feature vector, along with a rule-based binary classification method. In the next step, a holistic word recognition paradigm is used to confirm a pre-selected word as search word. To accomplish this, a modified histogram of oriented gradients-based feature descriptor is combined with a topological feature vector. This method is experimented on a QUWI English database, which is freely available through the International Conference on Document Analysis and Recognition 2015 competition entitled "Writer Identification and Gender Classification." This technique not only provides good retrieval performance in terms of recall, precision, and F-measure scores, but it also outperforms some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Due to the cursive nature, segmentation of handwritten Bangla words into characters and also recognition of the same sometimes become a very challenging problem to the researchers. Presence of comparatively large character set along with modifiers, ascendants, descendants, and compound characters makes the segmentation task more complex. As holistic method avoids such character-level segmentation, it is generally useful for the recognition of words written in any such complex scripts. In the present work, a holistic handwritten word recognition method is developed using a feature descriptor, designed by combining different Elliptical, Tetragonal and Vertical pixel density histogram-based features. Recognition process is carried out separately using two classifiers, namely multi-layer perceptron (MLP) and support vector machine (SVM). For evaluation of the proposed method, a database of 18,000 handwritten Bangla word images, having 120 word classes, is prepared. The proposed system performs comparatively better with SVM than MLP for the prepared dataset. It has achieved 83.64% accuracy at best case and 79.38% accuracy on an average using fivefold cross-validation. The current method has also outperformed some recently reported holistic word recognition technique tested on the developed dataset. In addition to that the database, prepared in this work, is made freely available to fill the absence of a publicly available standard database for holistic Bangla word recognition. [ABSTRACT FROM AUTHOR]