A novel approach for recognition of handwritten compound Bangla characters, along with the Basic characters of Bangla alphabet, is presented here. Compared to English like Roman script, one of the major stumbling blocks in Optical Character Recognition (OCR) of handwritten Bangla script is the large number of complex shaped character classes of Bangla alphabet. In addition to 50 basic character classes, there are nearly 160 complex shaped compound character classes in Bangla alphabet. Dealing with such a large varieties of handwritten characters with a suitably designed feature set is a challenging problem. Uncertainty and imprecision are inherent in handwritten script. Moreover, such a large varieties of complex shaped characters, some of which have close resemblance, makes the problem of OCR of handwritten Bangla characters more difficult. Considering the complexity of the problem, the present approach makes an attempt to identify compound character classes from most frequently to less frequently occurred ones, i.e., in order of importance. This is to develop a frame work for incrementally increasing the number of learned classes of compound characters from more frequently occurred ones to less frequently occurred ones along with Basic characters. On experimentation, the technique is observed produce an average recognition rate of 79.25 after three fold cross validation of data with future scope of improvement and extension.
Hajee Mohammad Danesh Science and Technology University（哈吉·穆罕默德·达内什科技大学）
Bangla Handwritten Digit recognition is a significant step forward in the development of Bangla OCR. However, intricate shape, structural likeness and distinctive composition style of Bangla digits makes it relatively challenging to distinguish. Thus, in this paper, we benchmarked four rigorous classifiers to recognize Bangla Handwritten Digit: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient-Boosted Decision Trees (GBDT) based on three handcrafted feature extraction techniques: Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and Gabor…
MCKV Institute of Engineering（MCKV工程学院）；Jadavpur University（贾达普大学）
The work presented here involves the design of a Multi Layer Perceptron (MLP) based pattern classifier for recognition of handwritten Bangla digits using a 76 element feature vector. Bangla is the second most popular script and language in the Indian subcontinent and the fifth most popular language in the world. The feature set developed for representing handwritten Bangla numerals here includes 24 shadow features, 16 centroid features and 36 longest-run features. On experimentation with a database of 6000 samples, the technique yields an average recognition rate of 96.67% evaluated after thre…
MCKV Institute of Engineering（MCKV工程学院）；Jadavpur University（贾达普大学）
The work presented here involves the design of a Multi Layer Perceptron (MLP) based classifier for recognition of handwritten Bangla alphabet using a 76 element feature set Bangla is the second most popular script and language in the Indian subcontinent and the fifth most popular language in the world. The feature set developed for representing handwritten characters of Bangla alphabet includes 24 shadow features, 16 centroid features and 36 longest-run features. Recognition performances of the MLP designed to work with this feature set are experimentally observed as 86.46% and 75.05% on the s…
Techno India College of Technology（泰克诺印度技术学院）；Jadavpur University（贾达普大学）
The objective of the paper is to recognize handwritten samples of basic Bangla characters using Tesseract open source Optical Character Recognition (OCR) engine under Apache License 2.0. Handwritten data samples containing isolated Bangla basic characters and digits were collected from different users. Tesseract is trained with user-specific data samples of document pages to generate separate user-models representing a unique language-set. Each such language-set recognizes isolated basic Bangla handwritten test samples collected from the designated users. On a three user model, the system is t…
India is a multi-lingual country where Roman script is often used alongside different Indic scripts in a text document. To develop a script specific handwritten Optical Character Recognition (OCR) system, it is therefore necessary to identify the scripts of handwritten text correctly. In this paper, we present a system, which automatically separates the scripts of handwritten words from a document, written in Bangla or Devanagri mixed with Roman scripts. In this script separation technique, we first, extract the text lines and words from document pages using a script independent Neighboring Co…
Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）；The Ohio State University（俄亥俄州立大学）；Jadavpur University（贾达普大学）
Finding local invariant patterns in handwrit-ten characters and/or digits for optical character recognition is a difficult task. Variations in writing styles from one person to another make this task challenging. We have proposed a non-explicit feature extraction method using a multi-scale multi-column skip convolutional neural network in this work. Local and global features extracted from different layers of the proposed architecture are combined to derive the final feature descriptor encoding a character or digit image. Our method is evaluated on four publicly available datasets of isolated …
Jadavpur University（jadavpur大学）；Indian Institute of Engineering Science and Technology, Shibpur（印度工程科学技术学院（希布普尔））
Segmentation of handwritten document images into text lines and words is one of the most significant and challenging tasks in the development of a complete Optical Character Recognition (OCR) system. This paper addresses the automatic segmentation of text words directly from unconstrained Bangla handwritten document images. The popular Distance transform (DT) algorithm is applied for locating the outer boundary of the word images. This technique is free from generating the over-segmented words. A simple post-processing procedure is applied to isolate the under-segmented word images, if any. Th…
Open Vocabulary Word Recognition From Transcribed Bangla Texts
Department of Computer Science；Engineering Khulna University of Engineering \& Technology Khulna-9203, Bangladesh；Engineering Khulna University of Engineering \& Technology Khulna-9203, Bangladesh Email: 1 3
Increased accuracy in predictive models for handwritten character recognition will open up new frontiers for optical character recognition. Major drawbacks of predictive machine learning models are headed by the elongated training time taken by some models, and the requirement that training and test data be in the same feature space and consist of the same distribution. In this study, these obstacles are minimized by presenting a model for transferring knowledge from one task to another. This model is presented for the recognition of handwritten numerals in Indic languages. The model utilizes …
Handwritten digit or numeral recognition is one of the classical issues in the area of pattern recognition and has seen tremendous advancement because of the recent wide availability of computing resources. Plentiful works have already done on English, Arabic, Chinese, Japanese handwritten script. Some work on Bangla also have been done but there is space for development. From that angle, in this paper, an architecture has been implemented which achieved the validation accuracy of 99.44% on BHAND dataset and outperforms Alexnet and Inception V3 architecture. Beside digit recognition, digit gen…
In a multilingual country like India where 12 different official scripts are in use, automatic identification of handwritten script facilitates many important applications such as automatic transcription of multilingual documents, searching for documents on the web/digital archives containing a particular script and for the selection of script specific Optical Character Recognition (OCR) system in a multilingual environment. In this paper, we propose a robust method towards identifying scripts from the handwritten documents at text line-level. The recognition is based upon features extracted u…
Louisiana State University（路易斯安那州立大学）；Ailectric LLC（Ailectric有限责任公司）
Classification techniques for images of handwritten characters are susceptible to noise. Quadtrees can be an efficient representation for learning from sparse features. In this paper, we improve the effectiveness of probabilistic quadtrees by using a pixel level classifier to extract the character pixels and remove noise from handwritten character images. The pixel level denoiser (a deep belief network) uses the map responses obtained from a pretrained CNN as features for reconstructing the characters eliminating noise. We experimentally demonstrate the effectiveness of our approach by reconst…
Color Independent Word Segmentation From Transcribed Bangla Passages
Department of Computer Science；Engineering Khulna University of Engineering \& Technology Khulna-9203, Bangladesh；Engineering Khulna University of Engineering \& Technology Khulna-9203, Bangladesh Email: 1 2 3
Acropolis Institute of Technology & Research（阿克罗波利斯技术与研究学院）
The reliance of humans over machines has never been so high such that from object classification in photographs to adding sound to silent movies everything can be performed with the help of deep learning and machine learning algorithms. Likewise, Handwritten text recognition is one of the significant areas of research and development with a streaming number of possibilities that could be attained. Handwriting recognition (HWR), also known as Handwritten Text Recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper docume…
Bangladesh University of Business & Technology（孟加拉商业与技术大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）
Optical character recognition (OCR) is a process of converting analogue documents into digital using document images. Currently, many commercial and non-commercial OCR systems exist for both handwritten and printed copies for different languages. Despite this, very few works are available in case of recognising Bengali words. Among them, most of the works focused on OCR of printed Bengali characters. This paper introduces an end-to-end OCR system for Bengali language. The proposed architecture implements an end to end strategy that recognises handwritten Bengali words from handwritten word ima…
Jadavpur University（贾达普大学）；The University of Iowa（爱荷华大学）
In dealing with the problem of recognition of handwritten character patterns of varying shapes and sizes, selection of a proper feature set is important to achieve high recognition performance. The current research aims to evaluate the performance of the convex hull based feature set, i.e. 125 features in all computed over different bays attributes of the convex hull of a pattern, for effective recognition of isolated handwritten Bangla basic characters and digits. On experimentation with a database of 10000 samples, the maximum recognition rate of 76.86% is observed for handwritten Bangla cha…
A considerable amount of success has been achieved in developing monolingual OCR systems for Indic scripts. But in a country like India, where multi-script scenario is prevalent, identifying scripts beforehand becomes obligatory. In this paper, we present the significance of Gabor wavelets filters in extracting directional energy and entropy distributions for 11 official handwritten scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Oriya, Tamil, Telugu, Urdu and Roman. The experimentation is conducted at block level based on a quad-tree decomposition approach and eva…
University of Dayton（代顿大学）；Saint Louis University（圣路易斯大学）；Comcast Labs（康卡斯特实验室）
In spite of advances in object recognition technology, Handwritten Bangla Character Recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even the best existing recognizers do not lead to satisfactory performance for practical applications related to Bangla character recognition and have much lower performance than those developed for English alpha-numeric characters. To improve the performance of HBCR, we herein present the application of the state-of-the-art Deep Convolutional Neural Networks (DCN…
In spite of the advances in pattern recognition technology, Handwritten Bangla Character Recognition (HBCR) (such as alpha-numeric and special characters) remains largely unsolved due to the presence of many perplexing characters and excessive cursive in Bangla handwriting. Even the best existing recognizers do not lead to satisfactory performance for practical applications. To improve the performance of Handwritten Bangla Digit Recognition (HBDR), we herein present a new approach based on deep neural networks which have recently shown excellent performance in many pattern recognition and mach…
In any multi-script environment, handwritten script classification is of paramount importance before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimension, thereby increasing the computation complexity of the whole classification model. Feature Selection (FS) can serve as an intermediate step to reduce the size of the feature vectors by restricting them only to the essential and relevant features. In ou…
Appropriate feature set for representation of pattern classes is one of the most important aspects of handwritten character recognition. The effectiveness of features depends on the discriminating power of the features chosen to represent patterns of different classes. However, discriminatory features are not easily measurable. Investigative experimentation is necessary for identifying discriminatory features. In the present work we have identified a new variation of feature set which significantly outperforms on handwritten Bangla alphabet from the previously used feature set. 132 number of f…
We introduce a new dataset for offline Handwritten Text Recognition (HTR) from images of Bangla scripts comprising words, lines, and document-level annotations. The BN-HTRd dataset is based on the BBC Bangla News corpus, meant to act as ground truth texts. These texts were subsequently used to generate the annotations that were filled out by people with their handwriting. Our dataset includes 788 images of handwritten pages produced by approximately 150 different writers. It can be adopted as a basis for various handwriting classification tasks such as end-to-end document recognition, word-spo…
Due to the sparsity of features, noise has proven to be a great inhibitor in the classification of handwritten characters. To combat this, most techniques perform denoising of the data before classification. In this paper, we consolidate the approach by training an all-in-one model that is able to classify even noisy characters. For classification, we progressively train a classifier generative adversarial network on the characters from low to high resolution. We show that by learning the features at each resolution independently a trained model is able to accurately classify characters even i…
B.P. Poddar Institute of Management and Technology（B.P. Poddar管理技术学院）；Government College of Leather Technology（皮革技术政府学院）；Government College of Engineering and Ceramic Technology（工程与陶瓷技术政府学院）；Washington University in Saint Louis（圣路易斯华盛顿大学）
In this paper, we propose a solution which uses state-of-the-art techniques in Deep Learning to tackle the problem of Bengali Handwritten Character Recognition ( HCR ). Our method uses lesser iterations to train than most other comparable methods. We employ Transfer Learning on ResNet 50, a state-of-the-art deep Convolutional Neural Network Model, pretrained on ImageNet dataset. We also use other techniques like a modified version of One Cycle Policy, varying the input image sizes etc. to ensure that our training occurs fast. We use the BanglaLekha-Isolated Dataset for evaluation of our techni…
Handwritten recognition (HWR) is the ability of a computer to receive and interpret intelligible handwritten input from source such as paper documents, photographs, touch-screens and other devices. In this paper we will using three (3) classification t o re cognize the handwritten which is SVM, KNN and Neural Network.
Dept. of Information Technology, Jadavpur University（贾达布尔大学信息技术系）；Dept. of Computer Science & Engg., Jadavpur University（贾达布尔大学计算机科学与工程系）
In this work, a novel deep learning technique for the recognition of handwritten Bangla isolated compound character is presented and a new benchmark of recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy layer wise training of Deep Neural Network has helped to make significant strides in various pattern recognition problems. We employ layerwise training to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and augment the training process with the RMSProp algorithm to achieve faster convergence. We compare results with those obtained from standard shallow le…
Identification of minimum number of local regions of a handwritten character image, containing well-defined discriminating features which are sufficient for a minimal but complete description of the character is a challenging task. A new region selection technique based on the idea of an enhanced Harmony Search methodology has been proposed here. The powerful framework of Harmony Search has been utilized to search the region space and detect only the most informative regions for correctly recognizing the handwritten character. The proposed method has been tested on handwritten samples of Bangl…
Jadavpur University（贾达夫普尔大学）；The University of Iowa（爱荷华大学）
Soft computing approaches are mainly designed to address the real world ill-defined, imprecisely formulated problems, combining different kind of novel models of computation, such as neural networks, genetic algorithms (GAs. Handwritten digit recognition is a typical example of one such problem. In the current work we have developed a two-pass approach where the first pass classifier performs a coarse classification, based on some global features of the input pattern by restricting the possibility of classification decisions within a group of classes, smaller than the number of classes conside…
Efficient approach of using CNN based pretrained model in Bangla handwritten digit recognition
Southeast University（东南大学）；Universität Rostock（罗斯托克大学）；University of Bedfordshire（贝德福特大学）；University of Portsmouth（朴茨茅斯大学）；University of Greenwich（格林威治大学）；Brandenburg University of Technology（勃兰登堡工业大学）；Shandong University of Finance and Economics（山东财经大学）
North South University（北南大学）；Independent University Bangladesh（孟加拉国独立大学）；City University of Hong Kong（香港城市大学）
Feature extraction is one of the fundamental problems of character recognition. The performance of character recognition system is depends on proper feature extraction and correct classifier selection. In this article, a rapid feature extraction method is proposed and named as Celled Projection (CP) that compute the projection of each section formed through partitioning an image. The recognition performance of the proposed method is compared with other widely used feature extraction methods that are intensively studied for many different scripts in literature. The experiments have been conduct…
Multi-Head Attention based interaction-aware architecture for Bangla Handwritten Character Recognition: Introducing a Primary Dataset
Department of Computer and Communication Engineering, International Islamic University Chittagong（国际伊斯兰大学吉大港分校计算机与通信工程系）；mPower Social Enterprise（mPower社会企业）；Department of Information and Communication Engineering, Noakhali Science and Technology University（诺阿卡利科技大学信息与通信工程系）；Department of CSE, Netrokona University（内特罗科纳大学计算机科学与工程系）；School of Computing Sciences and Computer Engineering, University of Southern Mississippi（南密西西比大学计算科学与计算机工程学院）
Meghnad Saha Institute of Technology（梅格纳德·萨哈技术学院）；Jadavpur University（贾达普大学）；G.H. Raisoni College of Engineering（G.H.赖索尼工程学院）
Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), multiple classifier combination, etc. In this paper, we discuss the characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten Devnagari characte…
A lot of search approaches have been explored for the selection of features in pattern classification domain in order to discover significant subset of the features which produces better accuracy. In this paper, we introduced a Harmony Search (HS) algorithm based feature selection method for feature dimensionality reduction in handwritten Bangla word recognition problem. This algorithm has been implemented to reduce the feature dimensionality of a technique described in one of our previous papers by S. Bhowmik et al.[1]. In the said paper, a set of 65 elliptical features were computed for hand…
School of Electrical Sciences, Indian Institute of Technology Bhubaneswar（印度理工学院布巴内斯瓦尔分校电气科学学院）
Feature representation in the form of spatio-spectral decomposition is one of the robust techniques adopted in automatic handwritten character recognition systems. In this regard, we propose a new image representation approach for unconstrained handwritten alphanumeric characters using sparse concept coded Tetrolets. Tetrolets, which does not use fixed dyadic square blocks for spectral decomposition like conventional wavelets, preserve the localized variations in handwritings by adopting tetrominoes those capture the shape geometry. The sparse concept coding of low entropy Tetrolet representat…
Multichannel Attention Networks with Ensembled Transfer Learning to Recognize Bangla Handwritten Charecter
Bangladesh Army University of Science and Technology (BAUST)（孟加拉国陆军科技大学）；University of Aizu（会津大学）；Pabna University of Science and Technology（pabna 科技大学）
Harbin Institute of Technology（哈尔滨工业大学）；The Chinese University of Hong Kong（香港中文大学）
Traditional approaches for handwritten Chinese character recognition suffer in classifying similar characters. In this paper, we propose to discriminate similar handwritten Chinese characters by using weakly supervised learning. Our approach learns a discriminative SVM for each similar pair which simultaneously localizes the discriminative region of similar character and makes the classification. For the first time, similar handwritten Chinese character recognition (SHCCR) is formulated as an optimization problem extended from SVM. We also propose a novel feature descriptor, Gradient Context, …
Recognition of Arabic characters is essential for natural language processing and computer vision fields. The need to recognize and classify the handwritten Arabic letters and characters are essentially required. In this paper, we present an algorithm for recognizing Arabic letters and characters based on using deep convolution neural networks (DCNN) and support vector machine (SVM). This paper addresses the problem of recognizing the Arabic handwritten characters by determining the similarity between the input templates and the pre-stored templates using both fully connected DCNN and dropout …
Handwritten character recognition is an active research challenge,especially for Indian scripts. This paper deals with handwritten Malayalam, with a complete set of basic characters, vowel and consonant signs and compound characters that may be present in the script. Spatial domain features suitable for recognition are chosen in this work. For classification, k-NN, SVM and ELM are employed
Various machine learning methods for writer independent recognition of Malayalam handwritten district names are discussed in this paper. Data collected from 56 different writers are used for the experiments. The proposed work can be used for the recognition of district in the address written in Malayalam. Different methods for Dimensionality reduction are discussed. Features consider for the recognition are Histogram of Oriented Gradient descriptor, Number of Black Pixels in the upper half and lower half, length of image. Classifiers used in this work are Neural Network, SVM and RandomForest.
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；Institute of Engineering & Management, Kolkata（加尔各答工程与管理学院）；Indian Statistical Institute, Kolkata（印度统计学院加尔各答分校）
This paper presents a novel approach towards Indic handwritten word recognition using zone-wise information. Because of complex nature due to compound characters, modifiers, overlapping and touching, etc., character segmentation and recognition is a tedious job in Indic scripts (e.g. Devanagari, Bangla, Gurumukhi, and other similar scripts). To avoid character segmentation in such scripts, HMM-based sequence modeling has been used earlier in holistic way. This paper proposes an efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, …
Handwriting recognition has been one of the most fascinating and challenging research areas in field of image processing and pattern recognition. It contributes enormously to the improvement of automation process. In this paper, a system for recognition of unconstrained handwritten Malayalam characters is proposed. A database of 10,000 character samples of 44 basic Malayalam characters is used in this work. A discriminate feature set of 64 local and 4 global features are used to train and test SVM classifier and achieved 92.24% accuracy
Kennesaw State University（肯尼索州立大学）；University of Calabria（卡拉布里亚大学）；North South University（南北大学）；University of Manitoba（曼尼托巴大学）
A handwritten word recognition system comes with issues such as lack of large and diverse datasets. It is necessary to resolve such issues since millions of official documents can be digitized by training deep learning models using a large and diverse dataset. Due to the lack of data availability, the trained model does not give the expected result. Thus, it has a high chance of showing poor results. This paper proposes a novel way of generating diverse handwritten word images using handwritten characters. The idea of our project is to train the BiLSTM-CTC architecture with generated synthetic…
Recognition of handwritten Roman characters and numerals has been extensively studied in the last few decades and its accuracy reached to a satisfactory state. But the same cannot be said while talking about the Devanagari script which is one of most popular script in India. This paper proposes an efficient digit recognition system for handwritten Devanagari script. The system uses a novel 196-element Mask Oriented Directional (MOD) features for the recognition purpose. The methodology is tested using five conventional classifiers on 6000 handwritten digit samples. On applying 3-fold cross-val…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；Institute of Engineering & Management（工程与管理学院）；Indian Statistical Institute（印度统计研究所）
This paper presents a novel approach to generate synthetic dataset for handwritten word recognition systems. It is difficult to recognize handwritten scripts for which sufficient training data is not readily available or it may be expensive to collect such data. Hence, it becomes hard to train recognition systems owing to lack of proper dataset. To overcome such problems, synthetic data could be used to create or expand the existing training dataset to improve recognition performance. Any available digital data from online newspaper and such sources can be used to generate synthetic data. In t…
Word-level handwritten optical character recognition (OCR) remains a challenge for morphologically rich languages like Bangla. The complexity arises from the existence of a large number of alphabets, the presence of several diacritic forms, and the appearance of complex conjuncts. The difficulty is exacerbated by the fact that some graphemes occur infrequently but remain indispensable, so addressing the class imbalance is required for satisfactory results. This paper addresses this issue by introducing two knowledge distillation methods: Leveraging Isolated Letter Accumulations By Ordering Tea…
University College of Rouzbehan（鲁兹贝汉大学学院）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Classification Ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. This study aims to improve the results of identifying the Persian handwritten letters using Error Correcting Output Coding (ECOC) ensemble method. Furthermore, the feature selection is used to reduce the costs of errors in our proposed method. ECOC is a method for decomposing a multi-way classification problem into many binary classification tasks; and then combining the results of the subtasks into a hypothesize…
An efficient, scalable and robust approach to the handwritten digits recognition problem based on the Saak transform is proposed in this work. First, multi-stage Saak transforms are used to extract a family of joint spatial-spectral representations of input images. Then, the Saak coefficients are used as features and fed into the SVM classifier for the classification task. In order to control the size of Saak coefficients, we adopt a lossy Saak transform that uses the principal component analysis (PCA) to select a smaller set of transform kernels. The handwritten digits recognition problem is …
Recognition of Persian handwritten characters has been considered as a significant field of research for the last few years under pattern analysing technique. In this paper, a new approach for robust handwritten Persian numerals recognition using strong feature set and a classifier fusion method is scrutinized to increase the recognition percentage. For implementing the classifier fusion technique, we have considered k nearest neighbour (KNN), linear classifier (LC) and support vector machine (SVM) classifiers. The innovation of this tactic is to attain better precision with few features using…
Faculty of Informatics, University of Gondar（贡达尔大学信息学院）
Recognition of handwritten document aims at transforming document images into a machine understandable format. Handwritten document recognition is the most challenging area in the field of pattern recognition. It becomes more complex when a document was written on vellum before hundreds of years, like older Geez scripts. In this study, we introduced a modified segmentation approach to recognize older Geez scripts. We used adaptive filtering for noise reduction, Isodata iterative global thresholding for document image binarization, modified bounding box projection to segment distinct strokes be…
