Thematische Bibliographien / Active learning in handwritten text recognition

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Auswahl der wissenschaftlichen Literatur zum Thema „Active learning in handwritten text recognition“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

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Zeitschriftenartikel zum Thema "Active learning in handwritten text recognition"

Maddineni, Bhavyasri. „Various Models for the Conversion of Handwritten Text to Digital Text“. International Journal for Research in Applied Science and Engineering Technology 9, Nr. VI (30.06.2021): 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, und Cheng-Lin Liu. „Learning confidence transformation for handwritten Chinese text recognition“. International Journal on Document Analysis and Recognition (IJDAR) 17, Nr. 3 (05.11.2013): 205–19. http://dx.doi.org/10.1007/s10032-013-0214-3.

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Wang, Yintong, Wenjie Xiao und Shuo Li. „Offline Handwritten Text Recognition Using Deep Learning: A Review“. Journal of Physics: Conference Series 1848, Nr. 1 (01.04.2021): 012015. http://dx.doi.org/10.1088/1742-6596/1848/1/012015.

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Khalkar, Rohini G., Adarsh Singh Dikhit und Anirudh Goel. „Handwritten Text Recognition using Deep Learning (CNN & RNN)“. IARJSET 8, Nr. 6 (30.06.2021): 870–81. http://dx.doi.org/10.17148/iarjset.2021.86148.

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Prabhanjan, S., und R. Dinesh. „Deep Learning Approach for Devanagari Script Recognition“. International Journal of Image and Graphics 17, Nr. 03 (Juli 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, Nr. 2 (11.04.2021): 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, und Norliza Noor. „A compact deep learning model for Khmer handwritten text recognition“. IAES International Journal of Artificial Intelligence (IJ-AI) 10, Nr. 3 (01.09.2021): 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 und Andreas Dengel. „A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT“. International Arab Journal of Information Technology 17, Nr. 3 (01.05.2020): 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 und 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, Nr. 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 und 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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Dissertationen zum Thema "Active learning in handwritten text recognition"

Hříbek, David. „Active Learning pro zpracování archivních pramenů“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445535.

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This work deals with the creation of a system that allows uploading and annotating scans of historical documents and subsequent active learning of models for character recognition (OCR) on available annotations (marked lines and their transcripts). The work describes the process, classifies the techniques and presents an existing system for character recognition. Above all, emphasis is placed on machine learning methods. Furthermore, the methods of active learning are explained and a method of active learning of available OCR models from annotated scans is proposed. The rest of the work deals with a system design, implementation, available datasets, evaluation of self-created OCR model and testing of the entire system.

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AlKhateeb, Jawad H. Y. „Word based off-line handwritten Arabic classification and recognition. Design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches“. Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4440.

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The design of a machine which reads unconstrained words still remains an unsolved problem. For example, automatic interpretation of handwritten documents by a computer is still under research. Most systems attempt to segment words into letters and read words one character at a time. However, segmenting handwritten words is very difficult. So to avoid this words are treated as a whole. This research investigates a number of features computed from whole words for the recognition of handwritten words in particular. Arabic text classification and recognition is a complicated process compared to Latin and Chinese text recognition systems. This is due to the nature cursiveness of Arabic text. The work presented in this thesis is proposed for word based recognition of handwritten Arabic scripts. This work is divided into three main stages to provide a recognition system. The first stage is the pre-processing, which applies efficient pre-processing methods which are essential for automatic recognition of handwritten documents. In this stage, techniques for detecting baseline and segmenting words in handwritten Arabic text are presented. Then connected components are extracted, and distances between different components are analyzed. The statistical distribution of these distances is then obtained to determine an optimal threshold for word segmentation. The second stage is feature extraction. This stage makes use of the normalized images to extract features that are essential in recognizing the images. Various method of feature extraction are implemented and examined. The third and final stage is the classification. Various classifiers are used for classification such as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition problem studied is the classification of 32492 words using ii the IFN/ENIT database. The results were promising and very encouraging in terms of improved baseline detection and word segmentation for further recognition. Moreover, several feature subsets were examined and a best recognition performance of 81.5% is achieved.

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AlKhateeb, Jawad Hasan Yasin. „Word based off-line handwritten Arabic classification and recognition : design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches“. Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4440.

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Serrano, Martínez-Santos Nicolás. „Interactive Transcription of Old Text Documents“. Doctoral thesis, Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/37979.

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Nowadays, there are huge collections of handwritten text documents in libraries all over the world. The high demand for these resources has led to the creation of digital libraries in order to facilitate the preservation and provide electronic access to these documents. However text transcription of these documents im- ages are not always available to allow users to quickly search information, or computers to process the information, search patterns or draw out statistics. The problem is that manual transcription of these documents is an expensive task from both economical and time viewpoints. This thesis presents a novel ap- proach for e cient Computer Assisted Transcription (CAT) of handwritten text documents using state-of-the-art Handwriting Text Recognition (HTR) systems. The objective of CAT approaches is to e ciently complete a transcription task through human-machine collaboration, as the e ort required to generate a manual transcription is high, and automatically generated transcriptions from state-of-the-art systems still do not reach the accuracy required. This thesis is centered on a special application of CAT, that is, the transcription of old text document when the quantity of user e ort available is limited, and thus, the entire document cannot be revised. In this approach, the objective is to generate the best possible transcription by means of the user e ort available. This thesis provides a comprehensive view of the CAT process from feature extraction to user interaction. First, a statistical approach to generalise interactive transcription is pro- posed. As its direct application is unfeasible, some assumptions are made to apply it to two di erent tasks. First, on the interactive transcription of hand- written text documents, and next, on the interactive detection of the document layout. Next, the digitisation and annotation process of two real old text documents is described. This process was carried out because of the scarcity of similar resources and the need of annotated data to thoroughly test all the developed tools and techniques in this thesis. These two documents were carefully selected to represent the general di culties that are encountered when dealing with HTR. Baseline results are presented on these two documents to settle down a benchmark with a standard HTR system. Finally, these annotated documents were made freely available to the community. It must be noted that, all the techniques and methods developed in this thesis have been assessed on these two real old text documents. Then, a CAT approach for HTR when user e ort is limited is studied and extensively tested. The ultimate goal of applying CAT is achieved by putting together three processes. Given a recognised transcription from an HTR system. The rst process consists in locating (possibly) incorrect words and employs the user e ort available to supervise them (if necessary). As most words are not expected to be supervised due to the limited user e ort available, only a few are selected to be revised. The system presents to the user a small subset of these words according to an estimation of their correctness, or to be more precise, according to their con dence level. Next, the second process starts once these low con dence words have been supervised. This process updates the recogni- tion of the document taking user corrections into consideration, which improves the quality of those words that were not revised by the user. Finally, the last process adapts the system from the partially revised (and possibly not perfect) transcription obtained so far. In this adaptation, the system intelligently selects the correct words of the transcription. As results, the adapted system will bet- ter recognise future transcriptions. Transcription experiments using this CAT approach show that this approach is mostly e ective when user e ort is low. The last contribution of this thesis is a method for balancing the nal tran- scription quality and the supervision e ort applied using our previously de- scribed CAT approach. In other words, this method allows the user to control the amount of errors in the transcriptions obtained from a CAT approach. The motivation of this method is to let users decide on the nal quality of the desired documents, as partially erroneous transcriptions can be su cient to convey the meaning, and the user e ort required to transcribe them might be signi cantly lower when compared to obtaining a totally manual transcription. Consequently, the system estimates the minimum user e ort required to reach the amount of error de ned by the user. Error estimation is performed by computing sepa- rately the error produced by each recognised word, and thus, asking the user to only revise the ones in which most errors occur. Additionally, an interactive prototype is presented, which integrates most of the interactive techniques presented in this thesis. This prototype has been developed to be used by palaeographic expert, who do not have any background in HTR technologies. After a slight ne tuning by a HTR expert, the prototype lets the transcribers to manually annotate the document or employ the CAT ap- proach presented. All automatic operations, such as recognition, are performed in background, detaching the transcriber from the details of the system. The prototype was assessed by an expert transcriber and showed to be adequate and e cient for its purpose. The prototype is freely available under a GNU Public Licence (GPL).
Serrano Martínez-Santos, N. (2014). Interactive Transcription of Old Text Documents [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37979
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Packer, Thomas L. „Scalable Detection and Extraction of Data in Lists in OCRed Text for Ontology Population Using Semi-Supervised and Unsupervised Active Wrapper Induction“. BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4258.

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Lists of records in machine-printed documents contain much useful information. As one example, the thousands of family history books scanned, OCRed, and placed on-line by FamilySearch.org probably contain hundreds of millions of fact assertions about people, places, family relationships, and life events. Data like this cannot be fully utilized until a person or process locates the data in the document text, extracts it, and structures it with respect to an ontology or database schema. Yet, in the family history industry and other industries, data in lists goes largely unused because no known approach adequately addresses all of the costs, challenges, and requirements of a complete end-to-end solution to this task. The diverse information is costly to extract because many kinds of lists appear even within a single document, differing from each other in both structure and content. The lists' records and component data fields are usually not set apart explicitly from the rest of the text, especially in a corpus of OCRed historical documents. OCR errors and the lack of document structure (e.g. HMTL tags) make list content hard to recognize by a software tool developed without a substantial amount of highly specialized, hand-coded knowledge or machine learning supervision. Making an approach that is not only accurate but also sufficiently scalable in terms of time and space complexity to process a large corpus efficiently is especially challenging. In this dissertation, we introduce a novel family of scalable approaches to list discovery and ontology population. Its contributions include the following. We introduce the first general-purpose methods of which we are aware for both list detection and wrapper induction for lists in OCRed or other plain text. We formally outline a mapping between in-line labeled text and populated ontologies, effectively reducing the ontology population problem to a sequence labeling problem, opening the door to applying sequence labelers and other common text tools to the goal of populating a richly structured ontology from text. We provide a novel admissible heuristic for inducing regular expression wrappers using an A* search. We introduce two ways of modeling list-structured text with a hidden Markov model. We present two query strategies for active learning in a list-wrapper induction setting. Our primary contributions are two complete and scalable wrapper-induction-based solutions to the end-to-end challenge of finding lists, extracting data, and populating an ontology. The first has linear time and space complexity and extracts highly accurate information at a low cost in terms of user involvement. The second has time and space complexity that are linear in the size of the input text and quadratic in the length of an output record and achieves higher F1-measures for extracted information as a function of supervision cost. We measure the performance of each of these approaches and show that they perform better than strong baselines, including variations of our own approaches and a conditional random field-based approach.

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Alabau, Gonzalvo Vicente. „Multimodal interactive structured prediction“. Doctoral thesis, Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/35135.

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This thesis presents scientific contributions to the field of multimodal interac- tive structured prediction (MISP). The aim of MISP is to reduce the human effort required to supervise an automatic output, in an efficient and ergonomic way. Hence, this thesis focuses on the two aspects of MISP systems. The first aspect, which refers to the interactive part of MISP, is the study of strate- gies for efficient human¿computer collaboration to produce error-free outputs. Multimodality, the second aspect, deals with other more ergonomic modalities of communication with the computer rather than keyboard and mouse. To begin with, in sequential interaction the user is assumed to supervise the output from left-to-right so that errors are corrected in sequential order. We study the problem under the decision theory framework and define an optimum decoding algorithm. The optimum algorithm is compared to the usually ap- plied, standard approach. Experimental results on several tasks suggests that the optimum algorithm is slightly better than the standard algorithm. In contrast to sequential interaction, in active interaction it is the system that decides what should be given to the user for supervision. On the one hand, user supervision can be reduced if the user is required to supervise only the outputs that the system expects to be erroneous. In this respect, we define a strategy that retrieves first the outputs with highest expected error first. Moreover, we prove that this strategy is optimum under certain conditions, which is validated by experimental results. On the other hand, if the goal is to reduce the number of corrections, active interaction works by selecting elements, one by one, e.g., words of a given output to be supervised by the user. For this case, several strategies are compared. Unlike the previous case, the strategy that performs better is to choose the element with highest confidence, which coincides with the findings of the optimum algorithm for sequential interaction. However, this also suggests that minimizing effort and supervision are contradictory goals. With respect to the multimodality aspect, this thesis delves into techniques to make multimodal systems more robust. To achieve that, multimodal systems are improved by providing contextual information of the application at hand. First, we study how to integrate e-pen interaction in a machine translation task. We contribute to the state-of-the-art by leveraging the information from the source sentence. Several strategies are compared basically grouped into two approaches: inspired by word-based translation models and n-grams generated from a phrase-based system. The experiments show that the former outper- forms the latter for this task. Furthermore, the results present remarkable improvements against not using contextual information. Second, similar ex- periments are conducted on a speech-enabled interface for interactive machine translation. The improvements over the baseline are also noticeable. How- ever, in this case, phrase-based models perform much better than word-based models. We attribute that to the fact that acoustic models are poorer estima- tions than morphologic models and, thus, they benefit more from the language model. Finally, similar techniques are proposed for dictation of handwritten documents. The results show that speech and handwritten recognition can be combined in an effective way. Finally, an evaluation with real users is carried out to compare an interactive machine translation prototype with a post-editing prototype. The results of the study reveal that users are very sensitive to the usability aspects of the user interface. Therefore, usability is a crucial aspect to consider in an human evaluation that can hinder the real benefits of the technology being evaluated. Hopefully, once usability problems are fixed, the evaluation indicates that users are more favorable to work with the interactive machine translation system than to the post-editing system.
Alabau Gonzalvo, V. (2014). Multimodal interactive structured prediction [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/35135
TESIS
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Kohút, Jan. „Aktivní učení pro rozpoznávání textu“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403210.

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The aim of this Master's thesis is to design methods of active learning and to experiment with datasets of historical documents. A large and diverse dataset IMPACT of more than one million lines is used for experiments. I am using neural networks to check the readability of lines and correctness of their annotations. Firstly, I compare architectures of convolutional and recurrent neural networks with bidirectional LSTM layer. Next, I study different ways of learning neural networks using methods of active learning. Mainly I use active learning to adapt neural networks to documents that the neural networks do not have in the original training dataset. Active learning is thus used for picking appropriate adaptation data. Convolutional neural networks achieve 98.6\% accuracy, recurrent neural networks achieve 99.5\% accuracy. Active learning decreases error by 26\% compared to random pick of adaptations data.

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Buchteile zum Thema "Active learning in handwritten text recognition"

Toselli, Alejandro Héctor, Enrique Vidal und Francisco Casacuberta. „Active Interaction and Learning in Handwritten Text Transcription“. In Multimodal Interactive Pattern Recognition and Applications, 119–33. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-479-1_5.

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Mestha, Punit, Shoaib Asif, Mansi Mayekar, Piyush Singh und Sonal Hutke. „Handwritten Text Line Recognition Using Deep Learning“. In Lecture Notes in Networks and Systems, 567–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84760-9_48.

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Inkeaw, Papangkorn, Jakramate Bootkrajang, Teresa Gonçalves und Jeerayut Chaijaruwanich. „Handwritten Character Recognition Using Active Semi-supervised Learning“. In Intelligent Data Engineering and Automated Learning – IDEAL 2018, 69–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03493-1_8.

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Zhang, Xu-Yao, Yi-Chao Wu, Fei Yin und Cheng-Lin Liu. „Deep Learning Based Handwritten Chinese Character and Text Recognition“. In Cognitive Computation Trends, 57–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06073-2_3.

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Guélorget, Paul, Bruno Grilheres und Titus Zaharia. „Deep Active Learning with Simulated Rationales for Text Classification“. In Pattern Recognition and Artificial Intelligence, 363–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_32.

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Van Tran, Cuong, Tuong Tri Nguyen, Dinh Tuyen Hoang, Dosam Hwang und Ngoc Thanh Nguyen. „Active Learning-Based Approach for Named Entity Recognition on Short Text Streams“. In Advances in Intelligent Systems and Computing, 321–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43982-2_28.

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Abdel Hady, Mohamed Farouk, und Friedhelm Schwenker. „Combining Committee-Based Semi-supervised and Active Learning and Its Application to Handwritten Digits Recognition“. In Multiple Classifier Systems, 225–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12127-2_23.

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Lee, Hong, Brijesh Verma, Michael Li und Ashfaqur Rahman. „Machine Learning Techniques in Handwriting Recognition“. In Machine Learning Algorithms for Problem Solving in Computational Applications, 12–29. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1833-6.ch002.

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Handwriting recognition is a process of recognizing handwritten text on a paper in the case of offline handwriting recognition and on a tablet in the case of online handwriting recognition and converting it into an editable text. In this chapter, the authors focus on offline handwriting recognition, which means that recognition system accepts a scanned handwritten page as an input and outputs an editable recognized text. Handwriting recognition has been an active research area for more than four decades, but some of the major problems still remained unsolved. Many techniques, including the machine learning techniques, have been used to improve the accuracy. This chapter focuses on describing the problems of handwriting recognition and presents the solutions using machine learning techniques for solving major problems in handwriting recognition. The chapter also reviews and presents the state of the art techniques with results and future research for improving handwriting recognition.

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Porwal, Utkarsh, Zhixin Shi und Srirangaraj Setlur. „Machine Learning in Handwritten Arabic Text Recognition“. In Handbook of Statistics - Machine Learning: Theory and Applications, 443–69. Elsevier, 2013. http://dx.doi.org/10.1016/b978-0-444-53859-8.00018-7.

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Jin, Lianwen, Weixin Yang, Ziyong Feng und Zecheng Xie. „Online Handwritten Chinese Character Recognition: From a Bayesian Approach to Deep Learning“. In Advances in Chinese Document and Text Processing, 79–126. WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813143685_0004.

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Konferenzberichte zum Thema "Active learning in handwritten text recognition"

Romero, Veronica, Joan Andreu Sanchez und Alejandre H. Toselli. „Active Learning in Handwritten Text Recognition using the Derivational Entropy“. In 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2018. http://dx.doi.org/10.1109/icfhr-2018.2018.00058.

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Nikitha, A., J. Geetha und D. S. JayaLakshmi. „Handwritten Text Recognition using Deep Learning“. In 2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT). IEEE, 2020. http://dx.doi.org/10.1109/rteict49044.2020.9315679.

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Serrano, Nicolás, Adrià Giménez, Albert Sanchis und Alfons Juan. „Active learning strategies for handwritten text transcription“. In International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1891903.1891962.

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Louradour, Jerome, und Christopher Kermorvant. „Curriculum Learning for Handwritten Text Line Recognition“. In 2014 11th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2014. http://dx.doi.org/10.1109/das.2014.38.

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Srinilta, Chutimet, und Suchakree Chatpoch. „Multi-task Learning and Thai Handwritten Text Recognition“. In 2020 6th International Conference on Engineering, Applied Sciences and Technology (ICEAST). IEEE, 2020. http://dx.doi.org/10.1109/iceast50382.2020.9165315.

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Zhu, Yuanping, Jun Sun und Satoshi Naoi. „Sub-structure Learning Based Handwritten Chinese Text Recognition“. In 2013 12th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2013. http://dx.doi.org/10.1109/icdar.2013.66.

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Alkhateeb, Jawad H., Aiman A. Turani und AbdulRahman A. Alsewari. „Performance of Machine Learning and Deep Learning on Arabic Handwritten Text Recognition“. In 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE). IEEE, 2020. http://dx.doi.org/10.1109/etcce51779.2020.9350863.

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Kumar, Gaurav, und Venu Govindaraju. „Bayesian Active Learning for Keyword Spotting in Handwritten Documents“. In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.356.

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de Sousa Neto, Arthur Flor, Byron Leite Dantas Bezerra, Alejandro Hector Toselli und Estanislau Baptista Lima. „HTR-Flor: A Deep Learning System for Offline Handwritten Text Recognition“. In 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2020. http://dx.doi.org/10.1109/sibgrapi51738.2020.00016.

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Wang, Zhen-Xing, Qiu-Feng Wang, Fei Yin und Cheng-Lin Liu. „Weakly Supervised Learning for Over-Segmentation Based Handwritten Chinese Text Recognition“. In 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2020. http://dx.doi.org/10.1109/icfhr2020.2020.00038.

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