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

Offline handwriting recognition—the automatic transcription of images of handwritten text—is a challenging task that combines computer vision with sequence learning. In most systems the two elements are handled separately, with sophisticated preprocessing techniques used to extract the image features and sequential models such as HMMs used to provide the transcriptions. By combining two recent innovations in neural networks—multidimensional recurrent neural networks and connectionist temporal classification—this paper introduces a globally trained offline handwriting recogniser that takes raw pixel data as input. Unlike competing systems, it does not require any alphabet specific preprocessing, and can therefore be used unchanged for any language. Evidence of its generality and power is provided by data from a recent international Arabic recognition competition, where it outperformed all entries (91.4% accuracy compared to 87.2% for the competition winner) despite the fact that neither author understands a word of Arabic.

/pdf/offline-handwriting-recognition-with-multidimensional-4710cm0ru0.pdf

Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks

Using recurrent neural network models for early detection of heart failure onset.

The automatic recognition of text on scanned images has enabled many applications such as searching for words in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images. This paper provides a comprehensive review of these methods. It is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed. It includes background on the field, discussion of the methods, and future research directions.

Offline Arabic handwriting recognition: a survey

Recurrent neural networks (RNNs) with Long Short-Term memory cells currently hold the best known results in unconstrained handwriting recognition. We show that their performance can be greatly improved using dropout - a recently proposed regularization method for deep architectures. While previous works showed that dropout gave superior performance in the context of convolutional networks, it had never been applied to RNNs. In our approach, dropout is carefully used in the network so that it does not affect the recurrent connections, hence the power of RNNs in modeling sequences is preserved. Extensive experiments on a broad range of handwritten databases confirm the effectiveness of dropout on deep architectures even when the network mainly consists of recurrent and shared connections.

/pdf/dropout-improves-recurrent-neural-networks-for-handwriting-2di6lmbs26.pdf

Dropout Improves Recurrent Neural Networks for Handwriting Recognition

This paper describes the Arabic handwriting recognition competition held at ICDAR 2007. This second competition (the first was at ICDAR 2005) again uses the IFN/ENIT-database with Arabic handwritten Tunisian town names. Today, more than 54 research groups from universities, research centers, and industry are working with this database worldwide. This year, 8 groups with 14 systems are participating in the competition. The systems were tested on known data and on two datasets which are unknown to the participants. The systems are compared on the most important characteristic, the recognition rate. Additionally, the relative speed of the different systems were compared. A short description of the participating groups, their systems, and the results achieved are finally presented.

/pdf/icdar-2005-arabic-handwriting-recognition-competition-9cf9s2k4p5.pdf

ICDAR 2005 Arabic handwriting recognition competition

This paper describes the handwriting recognition competitionheld at ICDAR 2009. This competition is based onthe RIMES-database, with French written text documents.These document are classified in three different categories,complete text pages, words, and isolated characters. Thisyear 10 systems were submitted for the handwritten recognitioncompetition on snippets of French words. The systemswere evaluated in three subtask depending of the sizes ofthe used dictionary. A comparison between different classificationand recognition systems show interesting results. Ashort description of the participating groups, their systems,and the results achieved are presented.

http://www.cvc.uab.es/icdar2009/papers/3725b398.pdf

ICDAR 2009 Handwriting Recognition Competition

This paper describes the Arabic handwriting recognition competition held at ICDAR 2009. This third competition (the first two were held at ICDAR 2005 and 2007, respectively) again used the IfN/ENIT-database with Arabic handwritten Tunisian town names. This very successful database is used today by more than 82 research groups from universities, research centers, and industries worldwide. At ICDAR 2009, 7 groups with 17 systems participated in the competition. The system evaluation was made on one known dataset and on two datasets unknown to the participants. The systems were compared based on the recognition rates achieved. Additionally, the relative speeds of the systems were compared. A description of the participating groups, their systems, and the results achieved are presented. As a very important result of this competition, a continuous improvement of the recognition rate from competition to competition of more than 5% can be observed.

ICDAR 2009-Arabic handwriting recognition competition

In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) after completion of the collection of 1000 handwritten forms written by 1000 writers from different countries. It is composed of an image database containing images of the written text at 200, 300, and 600 dpi resolutions, a manually verified ground truth database that contains meta-data describing the written text at the page, paragraph, and line levels. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. Preliminary experiments on Arabic handwritten text recognition are conducted using sample data from the database and the results are reported. The database will be made freely available to researchers world-wide for research in various handwritten-related problems such as text recognition, writer identification and verification, etc.

/pdf/khatt-arabic-offline-handwritten-text-database-26d98hrx6s.pdf

KHATT: Arabic Offline Handwritten Text Database

This Guide to OCR for Arabic Scripts is the first book of its kind, specifically devoted to this emerging field. Topics and features: contains contributions from the leading researchers in the field; with a Foreword by Professor Bente Maegaard of the University of Copenhagen; presents a detailed overview of Arabic character recognition technology, covering a range of different aspects of pre-processing and feature extraction; reviews a broad selection of varying approaches, including HMM-based methods and a recognition system based on multidimensional recurrent neural networks; examines the evaluation of Arabic script recognition systems, discussing data collection and annotation, benchmarking strategies, and handwriting recognition competitions; describes numerous applications of Arabic script recognition technology, from historical Arabic manuscripts to online Arabic recognition.

Haikal El Abed

Papers

ICDAR 2005 Arabic handwriting recognition competition

ICDAR 2009 Handwriting Recognition Competition

ICDAR 2009-Arabic handwriting recognition competition

KHATT: Arabic Offline Handwritten Text Database

Guide to OCR for Arabic Scripts