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Journal articles on the topic 'Active learning in handwritten text recognition'

Author: Grafiati
Published: 28 June 2021
Last updated: 2 February 2022

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1

Maddineni, Bhavyasri. "Various Models for the Conversion of Handwritten Text to Digital Text." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 2894–99. http://dx.doi.org/10.22214/ijraset.2021.35616.

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Handwritten Text Recognition (HTR) also known as Handwriting Recognition (HWR) is the detection and interpretation of handwritten text images by the computer. Handwritten text from various sources such as notebooks, documents, forms, photographs, and other devices can be given to the computer to predict and convert into the Computerized Text/Digital Text. Humans find easier to write on a piece of paper rather than typing, but now-a-days everything is being digitalized. So, HTR/HWR has an increasing use these days. There are various techniques used in recognizing the handwriting. Some of the traditional techniques are Character extraction, Character recognition, and Feature extraction, while the modern techniques are segmenting the lines for recognition, machine learning techniques, convolution neural networks, and recurrent neural networks. There are various applications for the HTR/HWR such as the Online recognition, Offline Recognition, Signature verification, Postal address interpretation, Bank-Cheque processing, Writer recognition and these are considered to be the active areas of research. An effective HTR/HWR is therefore needed for the above stated applications. During this project our objective is to find and develop various models of the purpose.
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Wang, Da-Han, and Cheng-Lin Liu. "Learning confidence transformation for handwritten Chinese text recognition." International Journal on Document Analysis and Recognition (IJDAR) 17, no. 3 (November 5, 2013): 205–19. http://dx.doi.org/10.1007/s10032-013-0214-3.

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Wang, Yintong, Wenjie Xiao, and Shuo Li. "Offline Handwritten Text Recognition Using Deep Learning: A Review." Journal of Physics: Conference Series 1848, no. 1 (April 1, 2021): 012015. http://dx.doi.org/10.1088/1742-6596/1848/1/012015.

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Khalkar, Rohini G., Adarsh Singh Dikhit, and Anirudh Goel. "Handwritten Text Recognition using Deep Learning (CNN & RNN)." IARJSET 8, no. 6 (June 30, 2021): 870–81. http://dx.doi.org/10.17148/iarjset.2021.86148.

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Prabhanjan, S., and R. Dinesh. "Deep Learning Approach for Devanagari Script Recognition." International Journal of Image and Graphics 17, no. 03 (July 2017): 1750016. http://dx.doi.org/10.1142/s0219467817500164.

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In this paper, we have proposed a new technique for recognition of handwritten Devanagari Script using deep learning architecture. In any OCR or classification system extracting discriminating feature is most important and crucial step for its success. Accuracy of such system often depends on the good feature representation. Deciding upon the appropriate features for classification system is highly subjective and requires lot of experience to decide proper set of features for a given classification system. For handwritten Devanagari characters it is very difficult to decide on optimal set of good feature to get good recognition rate. These methods use raw pixel values as features. Deep Learning architectures learn hierarchies of features. In this work, first image is preprocessed to remove noise, converted to binary image, resized to fixed size of 30[Formula: see text][Formula: see text][Formula: see text]40 and then convert to gray scale image using mask operation, it blurs the edges of the images. Then we learn features using an unsupervised stacked Restricted Boltzmann Machines (RBM) and use it with the deep belief network for recognition. Finally network weight parameters are fine tuned by supervised back propagation learning to improve the overall recognition performance. The proposed method has been tested on large set of handwritten numerical, character, vowel modifiers and compound characters and experimental results reveals that unsupervised method yields very good accuracy of (83.44%) and upon fine tuning of network parameters with supervised learning yields accuracy of (91.81%) which is the best results reported so far for handwritten Devanagari characters.
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Et. al., Kavitha Ananth,. "Handwritten Text Recognition using Deep Learning and Word Beam Search." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 2905–11. http://dx.doi.org/10.17762/turcomat.v12i2.2326.

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This paper offers a solution to traditional handwriting recognition techniques using concepts of Deep learning and Word Beam Search. This paper explains about how an individual handwritten word is classified from the handwritten text by translating into a digital form. The digital form when trained with the Connectionist Temporal Classification (CTC) loss function, the output produced is a RNN. This is a matrix containing character probabilities for each time-step. The final text is mapped using a CTC decoding algorithm by converting the character probabilities. The recognized text is constructed by a list of words from the dictionary by using the token passing algorithm. It is found the running time of token passing depends on the size of dictionary. Also the numbers like arbitrary character strings will not able to decode. In this paper the decoding search algorithm word beam search is proposed, in order to tackle these types of problems. This methodology support to constrain words similar to those contained in a dictionary. It allows the character strings such as arbitrary non-word between the words, and integrates into a word-level language model. It is found the running time is better when compared with the token passing. The proposed algorithm comprises of the decoding algorithm named vanilla beam search and token passing using the IAM dataset and Bentham data set.
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Annanurov, Bayram, and Norliza Noor. "A compact deep learning model for Khmer handwritten text recognition." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (September 1, 2021): 584. http://dx.doi.org/10.11591/ijai.v10.i3.pp584-591.

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<p>The motivation of this study is to develop a compact offline recognition model for Khmer handwritten text that would be successfully applied under limited access to high-performance computational hardware. Such a task aims to ease the ad-hoc digitization of vast handwritten archives in many spheres. Data collected for previous experiments were used in this work. The oneagainst-all classification was completed with state-of-the-art techniques. A compact deep learning model (2+1CNN), with two convolutional layers and one fully connected layer, was proposed. The recognition rate came out to be within 93-98%. The compact model is performed on par with the state-of-theart models. It was discovered that computational capacity requirements usually associated with deep learning can be alleviated, therefore allowing applications under limited computational power.</p>
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Ahmad, Riaz, Saeeda Naz, Muhammad Afzal, Sheikh Rashid, Marcus Liwicki, and Andreas Dengel. "A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT." International Arab Journal of Information Technology 17, no. 3 (May 1, 2020): 299–305. http://dx.doi.org/10.34028/iajit/17/3/3.

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This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.
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Nurseitov, Daniyar, Kairat Bostanbekov, Anel Alimova, Abdelrahman Abdallah, and Galymzhan Abdimanap. "Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models." Advances in Science, Technology and Engineering Systems Journal 5, no. 5 (2020): 934–43. http://dx.doi.org/10.25046/aj0505114.

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Dinges, Laslo, Ayoub Al-Hamadi, Moftah Elzobi, Sherif El-etriby, and Ahmed Ghoneim. "ASM Based Synthesis of Handwritten Arabic Text Pages." Scientific World Journal 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/323575.

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Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available.
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Amin, Muhammad Sadiq, Siddiqui Muhammad Yasir, and Hyunsik Ahn. "Recognition of Pashto Handwritten Characters Based on Deep Learning." Sensors 20, no. 20 (October 17, 2020): 5884. http://dx.doi.org/10.3390/s20205884.

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Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.
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GÜNTER, SIMON, and HORST BUNKE. "MULTIPLE CLASSIFIER SYSTEMS IN OFFLINE HANDWRITTEN WORD RECOGNITION — ON THE INFLUENCE OF TRAINING SET AND VOCABULARY SIZE." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 07 (November 2004): 1303–20. http://dx.doi.org/10.1142/s0218001404003678.

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Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. Recently, a number of classifier creation methods, known as ensemble methods, have been proposed in the field of machine learning. It has been shown that these methods are able to substantially improve recognition performance in complex classification tasks. In this paper we examine the influence of the vocabulary size and the number of training samples on the performance of three ensemble methods in the context of handwritten word recognition. The experiments were conducted with two different offline hidden Markov model based handwritten word recognizers.
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Et al., Dr S. K. Nivetha. "Recognition and Digitization of Handwritten Text using Histogram of Gradients and Artificial Neural Network." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 5, 2021): 2555–64. http://dx.doi.org/10.17762/turcomat.v12i6.5702.

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Handwriting recognition is one of the most persuasive and interesting projects as it is required in many real-life applications such as bank-check processing, postal-code recognition, handwritten notes or question paper digitization etc. Machine learning and deep learning methods are being used by developers to make computers more intelligent. A person learns how to execute a task by learning and repeating it over and over before it memorises the steps. The neurons in his brain will then be able to easily execute the task that he has mastered. This is also very close to machine learning. It employs a variety of architectures to solve various problems. Handwritten text recognition systems are models that capture and interpret handwritten numeric and character data from sources such as paper documents and photographs. For this application, a variety of machine learning algorithms were used. However, several limitations have been found, such as a large number of iterations, high training costs, and so on. Even though the other models have given impressive accuracy, it still has some drawbacks. In an unsupervised way, the Artificial Neural Network is used to learn effective data coding. For recognising real-world data, we built a model using Histogram of Oriented Gradients (HOG) and Artificial Neural Networks (ANN).
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Rizvi, S. S. R., A. Sagheer, K. Adnan, and A. Muhammad. "Optical Character Recognition System for Nastalique Urdu-Like Script Languages Using Supervised Learning." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (September 2019): 1953004. http://dx.doi.org/10.1142/s0218001419530045.

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There are two main techniques to convert written or printed text into digital format. The first technique is to create an image of written/printed text, but images are large in size so they require huge memory space to store, as well as text in image form cannot be undergo further processes like edit, search, copy, etc. The second technique is to use an Optical Character Recognition (OCR) system. OCR’s can read documents and convert manual text documents into digital text and this digital text can be processed to extract knowledge. A huge amount of Urdu language’s data is available in handwritten or in printed form that needs to be converted into digital format for knowledge acquisition. Highly cursive, complex structure, bi-directionality, and compound in nature, etc. make the Urdu language too complex to obtain accurate OCR results. In this study, supervised learning-based OCR system is proposed for Nastalique Urdu language. The proposed system evaluations under a variety of experimental settings apprehend 98.4% training results and 97.3% test results, which is the highest recognition rate ever achieved by any Urdu language OCR system. The proposed system is simple to implement especially in software front of OCR system also the proposed technique is useful for printed text as well as handwritten text and it will help in developing more accurate Urdu OCR’s software systems in the future.
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AL-Saffar, Ahmed, Suryanti Awang, Wafaa AL-Saiagh, Sabrina Tiun, and A. S. Al-khaleefa. "Deep Learning Algorithms for Arabic Handwriting Recognition: A Review." International Journal of Engineering & Technology 7, no. 3.20 (September 1, 2018): 344. http://dx.doi.org/10.14419/ijet.v7i3.20.19271.

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Computer vision (CV) refers to the study of the computer simulation of human visual science. Major task of CV is to collect images (or video) so that they could be used for analysis, gathering information, and making decisions or judgements. CV has greatly progressed and developed in the past few decades. In recent years, deep learning (DL) approaches have won several contests in pattern recognition and machine learning. (DL) dramatically improved the state-of-the-art in visual object recognition, object detection, handwritten recognition and many other domains. Handwritten recognition technique is one of this tasks that targeted to extract the text from documents or another images type. In contrast to the English domain, there are a limited works on the Arabic language that achieved satisfactory results, Due to the Arabic language cursive nature that induces many technical difficulties. This paper highlighted the pre-processing and binarization methods that have been used in the literature along with proposed numerous directions for developing. We review the various current deep learning approaches and tools used for Arabic handwritten recognition (AHWR), identified challenges along this line of this research, and gives several recommendations including a framework based (DL) that is particularly applicable for dealing with cursive nature languages.
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Attigeri, Savitha. "Neural Network based Handwritten Character Recognition system." International Journal Of Engineering And Computer Science 7, no. 03 (March 22, 2018): 23761–68. http://dx.doi.org/10.18535/ijecs/v7i3.18.

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Handwritten character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network. Each character data set contains 26 alphabets. Fifty different character data sets are used for training the neural network. The trained network is used for classification and recognition. In the proposed system, each character is resized into 30x20 pixels, which is directly subjected to training. That is, each resized character has 600 pixels and these pixels are taken as features for training the neural network. The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition
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Modi, Rohan. "Transcript Anatomization with Multi-Linguistic and Speech Synthesis Features." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1755–58. http://dx.doi.org/10.22214/ijraset.2021.35371.

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Handwriting Detection is a process or potential of a computer program to collect and analyze comprehensible input that is written by hand from various types of media such as photographs, newspapers, paper reports etc. Handwritten Text Recognition is a sub-discipline of Pattern Recognition. Pattern Recognition is refers to the classification of datasets or objects into various categories or classes. Handwriting Recognition is the process of transforming a handwritten text in a specific language into its digitally expressible script represented by a set of icons known as letters or characters. Speech synthesis is the artificial production of human speech using Machine Learning based software and audio output based computer hardware. While there are many systems which convert normal language text in to speech, the aim of this paper is to study Optical Character Recognition with speech synthesis technology and to develop a cost effective user friendly image based offline text to speech conversion system using CRNN neural networks model and Hidden Markov Model. The automated interpretation of text that has been written by hand can be very useful in various instances where processing of great amounts of handwritten data is required, such as signature verification, analysis of various types of documents and recognition of amounts written on bank cheques by hand.
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Elleuch, Mohamed, and Monji Kherallah. "Boosting of Deep Convolutional Architectures for Arabic Handwriting Recognition." International Journal of Multimedia Data Engineering and Management 10, no. 4 (October 2019): 26–45. http://dx.doi.org/10.4018/ijmdem.2019100102.

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In recent years, deep learning (DL) based systems have become very popular for constructing hierarchical representations from unlabeled data. Moreover, DL approaches have been shown to exceed foregoing state of the art machine learning models in various areas, by pattern recognition being one of the more important cases. This paper applies Convolutional Deep Belief Networks (CDBN) to textual image data containing Arabic handwritten script (AHS) and evaluated it on two different databases characterized by the low/high-dimension property. In addition to the benefits provided by deep networks, the system is protected against over-fitting. Experimentally, the authors demonstrated that the extracted features are effective for handwritten character recognition and show very good performance comparable to the state of the art on handwritten text recognition. Yet using Dropout, the proposed CDBN architectures achieved a promising accuracy rates of 91.55% and 98.86% when applied to IFN/ENIT and HACDB databases, respectively.
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Naidu, D. J. Samatha, and T. Mahammad Rafi. "HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS." International Journal of Computer Science and Mobile Computing 10, no. 8 (August 30, 2021): 41–45. http://dx.doi.org/10.47760/ijcsmc.2021.v10i08.007.

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Handwritten character Recognition is one of the active area of research where deep neural networks are been utilized. Handwritten character Recognition is a challenging task because of many reasons. The Primary reason is different people have different styles of handwriting. The secondary reason is there are lot of characters like capital letters, small letters & special symbols. In existing were immense research going on the field of handwritten character recognition system has been design using fuzzy logic and created on VLSI(very large scale integrated)structure. To Recognize the tamil characters they have use neural networks with the Kohonen self-organizing map(SOM) which is an unsupervised neural networks. In proposed system this project design a image segmentation based hand written character recognition system. The convolutional neural network is the current state of neural network which has wide application in fields like image, video recognition. The system easily identify or easily recognize text in English languages and letters, digits. By using Open cv for performing image processing and having tensor flow for training the neural network. To develop this concept proposing the innovative method for offline handwritten characters. detection using deep neural networks using python programming language.
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Miłosz, Marek, and Janusz Gazda. "Effectiveness of artificial neural networks in recognising handwriting characters." Journal of Computer Sciences Institute 7 (September 30, 2018): 210–14. http://dx.doi.org/10.35784/jcsi.680.

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Artificial neural networks are one of the tools of modern text recognising systems from images, including handwritten ones. The article presents the results of a computational experiment aimed at analyzing the quality of recognition of handwritten digits by two artificial neural networks (ANNs) with different architecture and parameters. The correctness indicator was used as the basic criterion for the quality of character recognition. In addition, the number of neurons and their layers and the ANNs learning time were analyzed. The Python language and the TensorFlow library were used to create the ANNs, and software for their learning and testing. Both ANNs were learned and tested using the same big sets of images of handwritten characters.
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Ajmire, P. E. "Offline Handwritten Devanagari Numeral Recognition Using Artificial Neural Network." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2016): 79. http://dx.doi.org/10.23956/ijarcsse.v7i8.27.

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Machine recognition of handwriting has been improving from last decay. The task of machine learning and recognition which also include reading handwriting is closely resembling human performance is still an open problem and also the central issue of an active field of research. Many researchers are working for fully automating the process of reading, understanding and interpretation of handwritten character. This research work proposes new approaches for extracting features in context of Handwritten Marathi numeral recognition. For classification technique Artificial Network is used. The overall accuracy of recognition of handwritten Devanagari numerals is 99.67% with SVM classifier, 99% with MLP and it is 98.13with GFF.
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Sharma, Shubhankar, and Vatsala Arora. "Script Identification for Devanagari and Gurumukhi using OCR." International Journal of Computer Science and Mobile Computing 10, no. 9 (September 30, 2021): 12–22. http://dx.doi.org/10.47760/ijcsmc.2021.v10i09.002.

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The study of character research is an active area for research as it pertains a lot of challenges. Various pattern recognition techniques are being used every day. As there are so many writing styles available, development of OCR (Optical Character Recognition) for handwritten text is difficult. Therefore, several measures have to be taken to improve the recognition process so that the burden of computation can be decreased and the accuracy for pattern recognition can be increased. The main objective of this review was to recognize and analyze handwritten document images. In this paper, we present a scheme to identify different Indian scripts like Devanagari and Gurumukhi.
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S J, Vivekanandan, and Dr Sivasubramanian S. "Handwritten Digit and Text Recognition Based On Convolutional Neural Network Approach." Journal of University of Shanghai for Science and Technology 23, no. 07 (July 1, 2021): 1518–25. http://dx.doi.org/10.51201/jusst/21/07330.

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The goal of this undertaking is to foster a powerful penmanship acknowledgment methods utilizing ideas of Machine learning and PC vision. An expansion of MNIST digits dataset called the Emnist dataset has been utilized. It contains 62 classes with 0-9 digits and A-Z characters in both capitalized and lowercase. To recognize transcribed content and convert it into computerized structure utilizing Convolutional Neural Network and Support Vector Machine, shortened as CNN and SVM, for text arrangement and identification, has been made. Before that we pre-prepared the dataset and applied different channels over it. Our framework will perceive the content precisely utilizing tensorflow libraries.
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Guo, Hang, Ji Wan, Haobin Wang, Hanxiang Wu, Chen Xu, Liming Miao, Mengdi Han, and Haixia Zhang. "Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition." Research 2021 (April 1, 2021): 1–9. http://dx.doi.org/10.34133/2021/4689869.

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Handwritten signatures widely exist in our daily lives. The main challenge of signal recognition on handwriting is in the development of approaches to obtain information effectively. External mechanical signals can be easily detected by triboelectric nanogenerators which can provide immediate opportunities for building new types of active sensors capable of recording handwritten signals. In this work, we report an intelligent human-machine interaction interface based on a triboelectric nanogenerator. Using the horizontal-vertical symmetrical electrode array, the handwritten triboelectric signal can be recorded without external energy supply. Combined with supervised machine learning methods, it can successfully recognize handwritten English letters, Chinese characters, and Arabic numerals. The principal component analysis algorithm preprocesses the triboelectric signal data to reduce the complexity of the neural network in the machine learning process. Further, it can realize the anticounterfeiting recognition of writing habits by controlling the samples input to the neural network. The results show that the intelligent human-computer interaction interface has broad application prospects in signature security and human-computer interaction.
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E.Kamalanaban, Dr, M. Gopinath, and S. Premkumar. "Medicine Box: Doctor’s Prescription Recognition Using Deep Machine Learning." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 114. http://dx.doi.org/10.14419/ijet.v7i3.34.18785.

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A Doctor’s prescription is a handwritten document written by doctors in the form of instructions that describes list of drugs for patients in time sickness, injuries and other disability problems. While we receiving a new prescription from doctor, it is unable to understand what drug name is prescribed on it. In most cases, however, we wouldn't be able to read it anyway because doctors use Latin abbreviations and medical terminologies on prescriptions that are not understandable by the general persons which make reading it very difficult. According to the National Academy of Sciences estimates that at least 1.5 million peoples are sickened, injured or killed each year by errors while reading prescription. This paper resolves the problems in doctor’s prescriptions through Medicine Box, and Smart phone application that uses Conventional Neural Network (CNN) to recognize handwritten medicine names and return readable digital text. This mobile application uses TensorFlow as the machine learning library, and Custom Repository to match the partial string with the drug name. With Medicine Box, cases of misinterpretation of medicine names can be decreased. This makes the ordinary persons to understand what doctor is prescribed in the prescription and also help for pharmacists.
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Mohammed, Mamoun Jassim, Suphian Mohammed Tariq, and Hayder Ayad. "Isolated Arabic handwritten words recognition using EHD and HOG methods." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (May 1, 2021): 801. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp801-808.

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<span>Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.</span>
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Gupta, Akanksha, Ravindra Pratap Narwaria, and Madhav Singh. "Review on Deep Learning Handwritten Digit Recognition using Convolutional Neural Network." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 245–47. http://dx.doi.org/10.35940/ijrte.e5287.019521.

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In this digital world, everything including documents, notes is kept in digital form. The requirement of converting these digital documents into processed information is in demand. This process is called as Handwritten digit recognition (HDR). The digital scan document is processed and classified to identify the hand written words into digital text so that it can be used to keep it in the documents format means in computerized font so that everybody can read it properly. In this paper, it is discussed that classifiers like KNN, SVM, CNN are used for HDR. These classifiers are trained with some predefined dataset and then used to process any digital scan document into computer document format. The scanned document is passed through four different stages for recognition where image is preprocessed, segmented and then recognized by classifier. MNIST dataset is used for training purpose. Complete CNN classifier is discussed in this paper. It is found that CNN is very accurate for HDR but still there is a scope to improve the performance in terms of accuracy, complexity and timing.
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Khan, Sulaiman, Habib Ullah Khan, and Shah Nazir. "Offline Pashto Characters Dataset for OCR Systems." Security and Communication Networks 2021 (July 27, 2021): 1–7. http://dx.doi.org/10.1155/2021/3543816.

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In computer vision and artificial intelligence, text recognition and analysis based on images play a key role in the text retrieving process. Enabling a machine learning technique to recognize handwritten characters of a specific language requires a standard dataset. Acceptable handwritten character datasets are available in many languages including English, Arabic, and many more. However, the lack of datasets for handwritten Pashto characters hinders the application of a suitable machine learning algorithm for recognizing useful insights. In order to address this issue, this study presents the first handwritten Pashto characters image dataset (HPCID) for the scientific research work. This dataset consists of fourteen thousand, seven hundred, and eighty-four samples—336 samples for each of the 44 characters in the Pashto character dataset. Such samples of handwritten characters are collected on an A4-sized paper from different students of Pashto Department in University of Peshawar, Khyber Pakhtunkhwa, Pakistan. On total, 336 students and faculty members contributed in developing the proposed database accumulation phase. This dataset contains multisize, multifont, and multistyle characters and of varying structures.
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Mukti, Mousumi Hasan, Quazi Saad-Ul-Mosaher, and Khalil Ahammad. "Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric." European Journal of Engineering Research and Science 3, no. 7 (July 31, 2018): 67. http://dx.doi.org/10.24018/ejers.2018.3.7.831.

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Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.
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Kim, Chang-Min, Ellen J. Hong, Kyungyong Chung, and Roy C. Park. "Line-segment Feature Analysis Algorithm Using Input Dimensionality Reduction for Handwritten Text Recognition." Applied Sciences 10, no. 19 (October 1, 2020): 6904. http://dx.doi.org/10.3390/app10196904.

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Recently, demand for handwriting recognition, such as automation of mail sorting, license plate recognition, and electronic memo pads, has exponentially increased in various industrial fields. In addition, in the image recognition field, methods using artificial convolutional neural networks, which show outstanding performance, have been applied to handwriting recognition. However, owing to the diversity of recognition application fields, the number of dimensions in the learning and reasoning processes is increasing. To solve this problem, a principal component analysis (PCA) technique is used for dimensionality reduction. However, PCA is likely to increase the accuracy loss due to data compression. Therefore, in this paper, we propose a line-segment feature analysis (LFA) algorithm for input dimensionality reduction in handwritten text recognition. This proposed algorithm extracts the line segment information, constituting the image of input data, and assigns a unique value to each segment using 3 × 3 and 5 × 5 filters. Using the unique values to identify the number of line segments and adding them up, a 1-D vector with a size of 512 is created. This vector is used as input to machine-learning. For the performance evaluation of the method, the Extending Modified National Institute of Standards and Technology (EMNIST) database was used. In the evaluation, PCA showed 96.6% and 93.86% accuracy with k-nearest neighbors (KNN) and support vector machine (SVM), respectively, while LFA showed 97.5% and 98.9% accuracy with KNN and SVM, respectively.
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Raja, Hiral, Aarti Gupta, and Rohit Miri. "Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques." European Journal of Engineering and Technology Research 6, no. 4 (May 29, 2021): 37–44. http://dx.doi.org/10.24018/ejers.2021.6.4.2460.

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The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.
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Devi, N. "Offline Handwritten Character Recognition using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1483–89. http://dx.doi.org/10.22214/ijraset.2021.37610.

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Abstract: This paper focuses on the task of recognizing handwritten Hindi characters using a Convolutional Neural Network (CNN) based. The recognized characters can then be stored digitally in the computer or used for other purposes. The dataset used is obtained from the UC Irvine Machine Learning Repository which contains 92,000 images divided into training (80%) and test set (20%). It contains different forms of handwritten Devanagari characters written by different individuals which can be used to train and test handwritten text recognizers. It contains four CNN layers followed by three fully connected layers for recognition. Grayscale handwritten character images are used as input. Filters are applied on the images to extract different features at each layer. This is done by the Convolution operation. The two other main operations involved are Pooling and Flattening. The output of the CNN layers is fed to the fully connected layers. Finally, the chance or probability score of each character is determined and the character with the highest probability score is shown as the output. A recognition accuracy of 98.94% is obtained. Similar models exist for the purpose, but the proposed model achieved a better performance and accuracy than some of the earlier models. Keywords: Devanagari characters, Convolutional Neural Networks, Image Processing
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Alan Jiju, Shaun Tuscano, and Chetana Badgujar. "OCR Text Extraction." International Journal of Engineering and Management Research 11, no. 2 (April 18, 2021): 83–86. http://dx.doi.org/10.31033/ijemr.11.2.11.

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This research tries to find out a methodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on – such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices.
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Cecotti, Hubert. "Active graph based semi-supervised learning using image matching: Application to handwritten digit recognition." Pattern Recognition Letters 73 (April 2016): 76–82. http://dx.doi.org/10.1016/j.patrec.2016.01.016.

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Xie, Zecheng, Zenghui Sun, Lianwen Jin, Hao Ni, and Terry Lyons. "Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 8 (August 1, 2018): 1903–17. http://dx.doi.org/10.1109/tpami.2017.2732978.

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Shibly, Mir Moynuddin Ahmed, Tahmina Akter Tisha, Tanzina Akter Tani, and Shamim Ripon. "Convolutional neural network-based ensemble methods to recognize Bangla handwritten character." PeerJ Computer Science 7 (June 28, 2021): e565. http://dx.doi.org/10.7717/peerj-cs.565.

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In this era of advancements in deep learning, an autonomous system that recognizes handwritten characters and texts can be eventually integrated with the software to provide better user experience. Like other languages, Bangla handwritten text extraction also has various applications such as post-office automation, signboard recognition, and many more. A large-scale and efficient isolated Bangla handwritten character classifier can be the first building block to create such a system. This study aims to classify the handwritten Bangla characters. The proposed methods of this study are divided into three phases. In the first phase, seven convolutional neural networks i.e., CNN-based architectures are created. After that, the best performing CNN model is identified, and it is used as a feature extractor. Classifiers are then obtained by using shallow machine learning algorithms. In the last phase, five ensemble methods have been used to achieve better performance in the classification task. To systematically assess the outcomes of this study, a comparative analysis of the performances has also been carried out. Among all the methods, the stacked generalization ensemble method has achieved better performance than the other implemented methods. It has obtained accuracy, precision, and recall of 98.68%, 98.69%, and 98.68%, respectively on the Ekush dataset. Moreover, the use of CNN architectures and ensemble methods in large-scale Bangla handwritten character recognition has also been justified by obtaining consistent results on the BanglaLekha-Isolated dataset. Such efficient systems can move the handwritten recognition to the next level so that the handwriting can easily be automated.
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Gifford, Nadia, Rafiq Ahmad, and Mario Soriano Morales. "Text Recognition and Machine Learning: For Impaired Robots and Humans." Alberta Academic Review 2, no. 2 (September 10, 2019): 31–32. http://dx.doi.org/10.29173/aar42.

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As robots and machines become more reliable, developing tools that utilize their potential in manufacturing and beyond is an important step being addressed by many, including the LIMDA team at the University of Alberta. A common and effective means to improve artificial performance is through optical character recognition methods. Within the category of artificial intelligence under classification machine learning, research has focussed on the benefits of convolutional neural networks (CNN) and the improvement provided compared to its parent method, neural networks. Neural networks serious flaw comes from memorization and the lack of learning about what the images contain, while CNN's combat those issues. CNN’s are designed to connect information received by the network and begins to closely mimic how humans experience learns. Using the programming language Python and machine learning libraries such as Tensorflow and Keras, different versions of CNN’s were tested against datasets containing low-resolution images with handwritten characters. The first two CNN’s were trained against the MNIST database against digits 0 through 9. The results from these tests illustrated the benefits of elements like max-pooling and the addition of convolutional layers. Taking that knowledge a final CNN was written to prove the accuracy of the algorithm against alphabet characters. After training and testings were complete the network showed an average 99.34% accuracy and 2.23% to the loss function. Time-consuming training epochs that don’t wield higher or more impressive results could also be eliminated. These and similar CNN’s have proven to yield positive results and in future research could be implemented into the laboratory to improve safety. Continuing to develop this work will lead to better translators for language, solid text to digital text transformation, and aides for the visual and speech impaired.
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Shanmugavel, Subramanian, Jagadeesh Kannan, Arjun Vaithilingam Sudhakar, and . "Handwritten Optical Character Extraction and Recognition from Catalogue Sheets." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 36. http://dx.doi.org/10.14419/ijet.v7i4.5.20005.

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The dataset consists of 20000 scanned catalogues of fossils and other artifacts compiled by the Geological Sciences Department. The images look like a scanned form filled with blue ink ball pen. The character extraction and identification is the first phase of the research and in the second phase we are planning to use the HMM model to extract the entire text from the form and store it in a digitized format. We used various image processing and computer vision techniques to extract characters from the 20000 handwritten catalogues. Techniques used for character extraction are Erode, MorphologyEx, Dilate, canny edge detection, find Counters, Counter Area etc. We used Histogram of Gradients (HOGs) to extract features from the character images and applied k-means and agglomerative clustering to perform unsupervised learning. This would allow us to prepare a labelled training dataset for the second phase. We also tried converting images from RGB to CMYK to improve k-means clustering performance. We also used thresholding to extract blue ink characters from the form after converting the image in HSV color format, but the background noise was significant, and results obtained were not promising. We are researching a more robust method to extract characters that doesn’t deform the characters and takes alignment into consideration.
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Mohd Kadir, Nasibah Husna, Sharifah Nur Syafiqah Mohd Nur Hidayah, Norasiah Mohammad, and Zaidah Ibrahim. "Comparison of convolutional neural network and bag of features for multi-font digit recognition." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (September 1, 2019): 1322. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1322-1328.

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<span>This paper evaluates the recognition performance of Convolutional Neural Network (CNN) and Bag of Features (BoF) for multiple font digit recognition. Font digit recognition is part of character recognition that is used to translate images from many document-input tasks such as handwritten, typewritten and printed text. BoF is a popular machine learning method while CNN is a popular deep learning method. Experiments were performed by applying BoF with Speeded-up Robust Feature (SURF) and Support Vector Machine (SVM) classifier and compared with CNN on Chars74K dataset. The recognition accuracy produced by BoF is just slightly lower than CNN where the accuracy of CNN is 0.96 while the accuracy of BoF is 0.94.</span>
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Wei, Qiang, Yukun Chen, Mandana Salimi, Joshua C. Denny, Qiaozhu Mei, Thomas A. Lasko, Qingxia Chen, et al. "Cost-aware active learning for named entity recognition in clinical text." Journal of the American Medical Informatics Association 26, no. 11 (July 11, 2019): 1314–22. http://dx.doi.org/10.1093/jamia/ocz102.

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Abstract Objective Active Learning (AL) attempts to reduce annotation cost (ie, time) by selecting the most informative examples for annotation. Most approaches tacitly (and unrealistically) assume that the cost for annotating each sample is identical. This study introduces a cost-aware AL method, which simultaneously models both the annotation cost and the informativeness of the samples and evaluates both via simulation and user studies. Materials and Methods We designed a novel, cost-aware AL algorithm (Cost-CAUSE) for annotating clinical named entities; we first utilized lexical and syntactic features to estimate annotation cost, then we incorporated this cost measure into an existing AL algorithm. Using the 2010 i2b2/VA data set, we then conducted a simulation study comparing Cost-CAUSE with noncost-aware AL methods, and a user study comparing Cost-CAUSE with passive learning. Results Our cost model fit empirical annotation data well, and Cost-CAUSE increased the simulation area under the learning curve (ALC) scores by up to 5.6% and 4.9%, compared with random sampling and alternate AL methods. Moreover, in a user annotation task, Cost-CAUSE outperformed passive learning on the ALC score and reduced annotation time by 20.5%–30.2%. Discussion Although AL has proven effective in simulations, our user study shows that a real-world environment is far more complex. Other factors have a noticeable effect on the AL method, such as the annotation accuracy of users, the tiredness of users, and even the physical and mental condition of users. Conclusion Cost-CAUSE saves significant annotation cost compared to random sampling.
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Can, Yekta Said, and M. Erdem Kabadayı. "Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers." Applied Sciences 10, no. 16 (August 6, 2020): 5430. http://dx.doi.org/10.3390/app10165430.

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Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.
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AL-Shatnawi, Atallah, Faisal Al-Saqqar, and Safa’a Alhusban. "A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 16 (September 22, 2020): 20. http://dx.doi.org/10.3991/ijim.v14i16.16005.

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<p class="0abstract">In this paper, we introduce a multi-stage offline holistic handwritten Arabic text recognition model using the Local Binary Pattern (LBP) technique and two machine-learning approaches; Support Vector Machines (SVM) and Artificial Neural Network (ANN). In this model, the LBP method is utilized for extracting the global text features without text segmentation. The suggested model was tested and utilized on version II of the IFN/ENIT database applying the polynomial, linear, and Gaussian SVM and ANN classifiers. Performance of the ANN was assessed using the Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) training methods. The classification outputs of the herein suggested model were compared and verified with the results obtained from two benchmark Arabic text recognition models (ATRSs) that are based on the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) methods using various normalization sizes of images of Arabic text. The classification outcomes of the suggested model are promising and better than the outcomes of the examined benchmarks models. The best classification accuracies of the suggested model (97.46% and 94.92%) are obtained using the polynomial SVM classifier and the BR ANN training methods, respectively.</p>
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Can, Yekta Said, and M. Erdem Kabadayı. "Automatic Estimation of Age Distributions from the First Ottoman Empire Population Register Series by Using Deep Learning." Electronics 10, no. 18 (September 13, 2021): 2253. http://dx.doi.org/10.3390/electronics10182253.

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Recently, an increasing number of studies have applied deep learning algorithms for extracting information from handwritten historical documents. In order to accomplish that, documents must be divided into smaller parts. Page and line segmentation are vital stages in the Handwritten Text Recognition systems; it directly affects the character segmentation stage, which in turn determines the recognition success. In this study, we first applied deep learning-based layout analysis techniques to detect individuals in the first Ottoman population register series collected between the 1840s and the 1860s. Then, we employed horizontal projection profile-based line segmentation to the demographic information of these detected individuals in these registers. We further trained a CNN model to recognize automatically detected ages of individuals and estimated age distributions of people from these historical documents. Extracting age information from these historical registers is significant because it has enormous potential to revolutionize historical demography of around 20 successor states of the Ottoman Empire or countries of today. We achieved approximately 60% digit accuracy for recognizing the numbers in these registers and estimated the age distribution with Root Mean Square Error 23.61.
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44

Hishimura, Kazuo, and Naotake Natori. "A Pratical Model to Simulate Human Handwriting and Its Application to Active Learning for Handwritten Character Recognition." IEEJ Transactions on Electronics, Information and Systems 116, no. 8 (1996): 936–42. http://dx.doi.org/10.1541/ieejeiss1987.116.8_936.

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Chen, Xiaoxue, Lianwen Jin, Yuanzhi Zhu, Canjie Luo, and Tianwei Wang. "Text Recognition in the Wild." ACM Computing Surveys 54, no. 2 (April 2021): 1–35. http://dx.doi.org/10.1145/3440756.

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The history of text can be traced back over thousands of years. Rich and precise semantic information carried by text is important in a wide range of vision-based application scenarios. Therefore, text recognition in natural scenes has been an active research topic in computer vision and pattern recognition. In recent years, with the rise and development of deep learning, numerous methods have shown promising results in terms of innovation, practicality, and efficiency. This article aims to (1) summarize the fundamental problems and the state-of-the-art associated with scene text recognition, (2) introduce new insights and ideas, (3) provide a comprehensive review of publicly available resources, and (4) point out directions for future work. In summary, this literature review attempts to present an entire picture of the field of scene text recognition. It provides a comprehensive reference for people entering this field and could be helpful in inspiring future research. Related resources are available at our GitHub repository: https://github.com/HCIILAB/Scene-Text-Recognition.
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Ahmed, Rami, Mandar Gogate, Ahsen Tahir, Kia Dashtipour, Bassam Al-tamimi, Ahmad Hawalah, Mohammed A. El-Affendi, and Amir Hussain. "Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts." Entropy 23, no. 3 (March 13, 2021): 340. http://dx.doi.org/10.3390/e23030340.

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Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we introduce a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we propose a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters. This aims to prevent overfitting and further enhance generalization performance when compared to conventional deep learning models. We employ a number of deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The model is extensively evaluated and shown to demonstrate excellent classification accuracy when compared to conventional OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). A further experimental study is conducted on the benchmark Arabic databases by exploiting transfer learning (TL)-based feature extraction which demonstrates the superiority of our proposed model in relation to state-of-the-art VGGNet-19 and MobileNet pre-trained models. Finally, experiments are conducted to assess comparative generalization capabilities of the models using another language database , specifically the benchmark MNIST English isolated Digits database, which further confirm the superiority of our proposed DCNN model.
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Adeyanju, Ibrahim, Olusayo Fenwa, and Elijah Omidiora. "EFFECT OF NON-IMAGE FEATURES ON RECOGNITION OF HANDWRITTEN ALPHA-NUMERIC CHARACTERS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 11 (November 30, 2014): 5155–61. http://dx.doi.org/10.24297/ijct.v13i11.2785.

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Handwritten character recognition has applications in several industries such as Banking for reading of cheques and Libraries/ National archives for digital searchable storage of historic texts. The main feature typically used for the recognition task is the character image. However, there are other possible features such as the hand (left or right) used by author, number of strokes and other geometric features that can be captured when writing on digital devices. This paper investigates the effect of using some non-image features on the recognition rate of three classifiers: Instance Based Learner (IBk), Support Vector Machines (SVM) and the Multilayer Perceptron (MLP) Neural Network for singly-written alpha-numeric character recognition. Our experiments were conducted using the WEKA machine learning tool on offline and online handwritten acquired locally. A percentage split (66%-34% train-test) evaluation methodology was adopted with the classification accuracy measured. Results indicate that non-image additional features improved the accuracy across the three classifiers for the online and offline character datasets. However, this improvement was not statistically significant. SVM gave the best accuracy for the online dataset while IBk performed better than the other two classifiers for the offline dataset. We intend to investigate the effect of non-image features at other levels of text granularity such as words and sentences
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Qiu, Ningjia, Lin Cong, Sicheng Zhou, and Peng Wang. "Barrage Text Classification with Improved Active Learning and CNN." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 6 (November 20, 2019): 980–89. http://dx.doi.org/10.20965/jaciii.2019.p0980.

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Traditional convolutional neural networks (CNNs) use a pooling layer to reduce the dimensionality of texts, but lose semantic information. To solve this problem, this paper proposes a convolutional neural network model based on singular value decomposition algorithm (SVD-CNN). First, an improved density-based center point clustering active learning sampling algorithm (DBC-AL) is used to obtain a high-quality training set at a low labelling cost. Second, the method uses the singular value decomposition algorithm for feature extraction and dimensionality reduction instead of a pooling layer, fuses the dimensionality reduction matrix, and completes the barrage text classification task. Finally, the partial sampling gradient descent algorithm (PSGD) is applied to optimize the model parameters, which accelerates the convergence speed of the model while ensuring stability of the model training. To verify the effectiveness of the improved algorithm, several barrage datasets were used to compare the proposed model and common text classification models. The experimental results show that the improved algorithm preserves the semantic features of the text more successfully, ensures the stability of the training process, and improves the convergence speed of the model. Further, the model’s classification performance on different barrage texts is superior to traditional algorithms.
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Chen, Yukun, Thomas A. Lasko, Qiaozhu Mei, Joshua C. Denny, and Hua Xu. "A study of active learning methods for named entity recognition in clinical text." Journal of Biomedical Informatics 58 (December 2015): 11–18. http://dx.doi.org/10.1016/j.jbi.2015.09.010.

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Rizvi, Murtaza Abbas, Madhup Shrivastava, and Monika Sahu. "ARTIFICIAL NEURAL NETWORK BASED CHARACTER RECOGNITION USING BACKPROPAGAT." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (August 1, 2012): 184–87. http://dx.doi.org/10.24297/ijct.v3i1c.2777.

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Optical Character Recognition, or OCR, is a technology that enables you to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data format. OCR is the translation of optically scanned bitmap of printed or written text character into the character codes, such as ASCII. This is an efficient way to turn hard copy material into digital data files that can be edited or manipulated. The optical character recognition refers to the branch of computer science that involves reading text from paper and translating the images into a form that the computer can manipulate. The potential of this technology is typically used for general character recognition which includes the transformation of anything humanly readable to machine manipulatable representation. OCR systems are enormous because they enable users to harness the power of computers to access printed documents. The aim of this paper is to find a means by which the database entry from handwritten forms can be automated. Firstly the paper deals with the technology scanning hard copy data. Secondly describes machine learning process for training the system for converting hard copy into soft copy
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