Recurrent neural networks are powerful sequence learners. They are able to incorporate context information in a flexible way, and are robust to localised distortions of the input data. These properties make them well suited to sequence labelling, where input sequences are transcribed with streams of labels. The aim of this thesis is to advance the state-of-the-art in supervised sequence labelling with recurrent networks. Its two main contributions are (1) a new type of output layer that allows recurrent networks to be trained directly for sequence labelling tasks where the alignment between the inputs and the labels is unknown, and (2) an extension of the long short-term memory network architecture to multidimensional data, such as images and video sequences.

https://mediatum.ub.tum.de/doc/673554/file.pdf

Supervised Sequence Labelling with Recurrent Neural Networks

This paper introduces a pair of online and offline Chinese handwriting databases, containing samples of isolated characters and handwritten texts. The samples were produced by 1,020 writers using Anoto pen on papers for obtaining both online trajectory data and offline images. Both the online samples and offline samples are divided into six datasets, three for isolated characters (DB1.0-C1.2) and three for handwritten texts (DB2.0-C2.2). The (either online or offline) datasets of isolated characters contain about 3.9 million samples of 7,356 classes (7,185 Chinese characters and 171 symbols), and the datasets of handwritten texts contain about 5,090 pages and 1.35 million character samples. Each dataset is segmented and annotated at character level, and is partitioned into standard training and test subsets. The online and offline databases can be used for the research of various handwritten document analysis tasks.

/pdf/casia-online-and-offline-chinese-handwriting-databases-u25ema1eot.pdf

CASIA Online and Offline Chinese Handwriting Databases

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence/machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

http://www.cenparmi.concordia.ca/ICFHR2008/Proceedings/papers/cr1027.pdf

A new benchmark on the recognition of handwritten Bangla and Farsi numeral characters

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2018. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 142 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

This paper describes an important step towards the standardization of the research on Optical Character Recognition (OCR) in Farsi language. It describes formations of novel and standard handwritten databases including isolated digits, letters, numerical strings, Legal amounts (used for cheques), and dates. Despite conventional research and an Internet search, no publicly accessible Farsi database was found. Hence, it was decided that it would be a worthwhile academic effort to create several Farsi databases that could stand on their own merit functioning as useful tools for OCR researchers. Also, in order to show the potential uses of our new databases we also conducted some experiments on the recognition of handwritten isolated Farsi digits.

https://hal.inria.fr/inria-00103983/document

Standard Databases for Recognition of Handwritten Digits, Numerical Strings, Legal Amounts, Letters and Dates in Farsi Language

This paper presents the history of the Persian (Farsi) script, as well as the development of different writing styles for the current Persian script. It also addresses the Arabic alphabet adopted and evolved for writing the Persian language as well as different writing styles. This evolution includes further extensions to the Arabic alphabet and altered shapes of some Arabic letters. The differences between current Arabic and Persian handwritings for automated recognition purposes are discussed. A short review on techniques in Persian script recognition is presented, and the shortcomings and challenges in this area are highlighted.

A review on Persian script and recognition techniques

In this paper, a brief history of the evolution of Farsi (Persian) script is presented, including how the Farsi alphabet was derived from the Arabic alphabet. Important features, similarities and dissimilarities between Farsi and Arabic scripts, from the Optical Character Recognition (OCR) point of view are discussed. Also, a brief review of some of the state-of-the-art techniques in off-line and on-line Farsi script recognition and segmentation, as well as challenges ahead are briefly presented.

/pdf/state-of-the-art-in-farsi-script-recognition-18amkc6exp.pdf

Farshid Solimanpour

Papers

Standard Databases for Recognition of Handwritten Digits, Numerical Strings, Legal Amounts, Letters and Dates in Farsi Language

A review on Persian script and recognition techniques

State-of-the-art in Farsi script recognition