879 research outputs found
Word Recognition with Deep Conditional Random Fields
Recognition of handwritten words continues to be an important problem in
document analysis and recognition. Existing approaches extract hand-engineered
features from word images--which can perform poorly with new data sets.
Recently, deep learning has attracted great attention because of the ability to
learn features from raw data. Moreover they have yielded state-of-the-art
results in classification tasks including character recognition and scene
recognition. On the other hand, word recognition is a sequential problem where
we need to model the correlation between characters. In this paper, we propose
using deep Conditional Random Fields (deep CRFs) for word recognition.
Basically, we combine CRFs with deep learning, in which deep features are
learned and sequences are labeled in a unified framework. We pre-train the deep
structure with stacked restricted Boltzmann machines (RBMs) for feature
learning and optimize the entire network with an online learning algorithm. The
proposed model was evaluated on two datasets, and seen to perform significantly
better than competitive baseline models. The source code is available at
https://github.com/ganggit/deepCRFs.Comment: 5 pages, published in ICIP 2016. arXiv admin note: substantial text
overlap with arXiv:1412.339
Premature thoughts on writing disorders
Three papers appeared in the 19th century describing the dissociation between speech and writing: Marce (1856), Ogle (1867) and Pitres (1884). An account of the convincing evidence of dissociations put forward in these papers is presented. Three explanations are proposed as to the reason why the observations reported by these authors were overlooked or rejected by their contemporaries, namely: (a) in the first half of the century it seems that very little knowledge of the processes underlying writing (as opposed to speech) was available, (b) the debates focussed on the independence of speech versus motor control and language versus the intellect, (c) parallelisms between phylogeny, ontogeny and aphasia impeded the application of the principle of double dissociations, including the dissociations between speech and writing. It is argued that this phenomenon in the history of aphasia is best captured by the concept of prematurity in scientific discovery proposed by Stent (1972, 2003)
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Use of colour for hand-filled form analysis and recognition
Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis
Automatic analysis of scanned historical documents comprises a wide range of
image analysis tasks, which are often challenging for machine learning due to a
lack of human-annotated learning samples. With the advent of deep neural
networks, a promising way to cope with the lack of training data is to
pre-train models on images from a different domain and then fine-tune them on
historical documents. In the current research, a typical example of such
cross-domain transfer learning is the use of neural networks that have been
pre-trained on the ImageNet database for object recognition. It remains a
mostly open question whether or not this pre-training helps to analyse
historical documents, which have fundamentally different image properties when
compared with ImageNet. In this paper, we present a comprehensive empirical
survey on the effect of ImageNet pre-training for diverse historical document
analysis tasks, including character recognition, style classification,
manuscript dating, semantic segmentation, and content-based retrieval. While we
obtain mixed results for semantic segmentation at pixel-level, we observe a
clear trend across different network architectures that ImageNet pre-training
has a positive effect on classification as well as content-based retrieval
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images
There are two types of information in each handwritten word image: explicit
information which can be easily read or derived directly, such as lexical
content or word length, and implicit attributes such as the author's identity.
Whether features learned by a neural network for one task can be used for
another task remains an open question. In this paper, we present a deep
adaptive learning method for writer identification based on single-word images
using multi-task learning. An auxiliary task is added to the training process
to enforce the emergence of reusable features. Our proposed method transfers
the benefits of the learned features of a convolutional neural network from an
auxiliary task such as explicit content recognition to the main task of writer
identification in a single procedure. Specifically, we propose a new adaptive
convolutional layer to exploit the learned deep features. A multi-task neural
network with one or several adaptive convolutional layers is trained
end-to-end, to exploit robust generic features for a specific main task, i.e.,
writer identification. Three auxiliary tasks, corresponding to three explicit
attributes of handwritten word images (lexical content, word length and
character attributes), are evaluated. Experimental results on two benchmark
datasets show that the proposed deep adaptive learning method can improve the
performance of writer identification based on single-word images, compared to
non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio
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