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Text/Non-text Image Classification in the Wild with Convolutional Neural Networks

Abstract and Figures

Text in natural images is an important source of information, which can be utilized for many real-world applications. This work focuses on a new problem: distinguishing images that contain text from a large volume of natural images. To address this problem, we propose a novel convolutional neural network variant, called Multi-scale Spatial Partition Network (MSP-Net). The network classifies images that contain text or not, by predicting text existence in all image blocks, which are spatial partitions at multiple scales on an input image. The whole image is classified as a text image (an image containing text) as long as one of the blocks is predicted to contain text. The network classifies images very efficiently by predicting all blocks simultaneously in a single forward propagation. Through experimental evaluations and comparisons on public datasets, we demonstrate the effectiveness and robustness of the proposed method.
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Author’s Accepted Manuscript
Text/Non-text Image Classification in the Wild
with Convolutional Neural Networks
Xiang Bai, Baoguang Shi, Chengquan Zhang, Xuan
Cai, Li Qi
PII: S0031-3203(16)30392-2
Reference: PR5977
To appear in: Pattern Recognition
Received date: 13 March 2016
Revised date: 5 December 2016
Accepted date: 8 December 2016
Cite this article as: Xiang Bai, Baoguang Shi, Chengquan Zhang, Xuan Cai and
Li Qi, Text/Non-text Image Classification in the Wild with Convolutional Neural
Networks, Pattern Recognition,
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Text/Non-text Image Classification in the Wild with
Convolutional Neural Networks
Xiang Baia, Baoguang Shia, Chengquan Zhanga, Xuan Caib, Li Qib,
aSchool of Electronic Information and Communications, Huazhong University of Science
and Technology, Wuhan, China 430074
bThe Third Research Institute of the Ministry of Public Security, Shanghai, China
Text in natural images is an important source of information, which can be
utilized for many real-world applications. This work focuses on a new problem:
distinguishing images that contain text from a large volume of natural images.
To address this problem, we propose a novel convolutional neural network vari-
ant, called Multi-scale Spatial Partition Network (MSP-Net). The network clas-
sifies images that contain text or not, by predicting text existence in all image
blocks, which are spatial partitions at multiple scales on an input image. The
whole image is classified as a text image (an image containing text) as long as
one of the blocks is predicted to contain text. The network classifies images very
efficiently by predicting all blocks simultaneously in a single forward propaga-
tion. Through experimental evaluations and comparisons on public datasets,
we demonstrate the effectiveness and robustness of the proposed method.
Keywords: Natural images, Text/non-text image classification, Convolutional
neural network, Multi-scale spatial partition
1. Introduction
Scene text is an important source of information that is helpful for many
real-world applications, including image retrieval, human-computer interaction,
Corresponding author
Email addresses: (Xiang Bai),
(Baoguang Shi), (Chengquan Zhang),
(Xuan Cai), (Li Qi)
Preprint submitted to Pattern Recognition December 10, 2016
blind assistance system, transportation navigation, etc. Therefore, scene text
reading, which includes text detection and recognition, has attracted much at-5
tention in the community [1, 2, 3]. However, typically, in a large volume of
natural images and video data, only a small portion contains text. In our es-
timation on an image dataset collected from social networks, only 10%-15% of
the images contain text. Directly applying scene text reading algorithms for
mining textual information tends to be inefficient, as most of the existing text10
reading algorithms are time-consuming. To precisely localize text in an image,
algorithms like [4, 5, 6, 7, 8] typically require searching a large set of text-line
or character candidates, or dense image patches. The search would be mean-
ingless if an image contains no text at all. Therefore, an efficient preprocessing
algorithm that quickly distinguishes whether an image contains text or not is15
desirable, which can be utilized as an essential stage of the systems for text
reading or script identification [9].
In this work, we address a relatively new problem: text/non-text image clas-
sification in the wild. The image that contains text is identified as text image (or
text positive image), regardless of the scale or location of text in it. Whereas,20
the image that does not contain any text is named as non-text image (or text
negative image). In this paper, we adopt the pair of text image and non-text
image to distinguish two types of natural images. We define text image as an
image that contains text, regardless of its scale or location, and non-text image
as an image that contains no text at all. Although some previous works have25
already addressed the text/non-text image classification problem, their focus
is mainly on video frames [10, 11], document images [12], or handwriting im-
ages [13, 14]. However, we focus on the discrimination of text/non-text natural
images, which has been seldom studied.
Unlike scene text detection, text/non-text image classification neither re-30
quires finding precise text locations, nor recognizing text contents. Instead,
computational efficiency is important. A text/non-text image classification al-
gorithm should classify a large amount of images in a short period of time, while
achieving high precision and recall.
Figure 1: Examples of text/non-text images. (a) Text images contain at least one piece of
scene text, regardless of the scales and locations; (b) Non-text images contain no text at all.
We argue that the proposed problem is challenging in four aspects. First,35
scene text exhibits large variations in font, scale, color, orientation, illumination,
and language type. The examples shown in Fig. 1 demonstrate some of the
variations. Second, difficult to distinguish scene text with other background
objects, such as windows, grass, and fences, which are similar to text. Third, the
locations of scene text are not known in advance. It may appear at any position40
in an image. Last, a text/non-text image classification algorithm should work
efficiently enough to process a large amount of data in a reasonable period of
Essentially, text/non-text image classification is a binary classification prob-
lem. A straight-forward solution is to fine-tune some well-trained image classi-45
fiers, such as the Convolutional Neural Network (CNN) model proposed in [15].
However, due to the above-mentioned challenges, general image classification al-
gorithms may not work well for this problem. In particular, conventional CNN
models do not explicitly handle large scale and location variations exhibited in
scene text.50
In this paper, we propose a novel variant of CNN, named Multi-scale Spatial
Partition Network (MSP-Net), which is specially designed for the problem of
text/non-text image classification. The main idea is to classify all image blocks,
which are regions produced by multi-scale spatial partition on an input image. If
at least one of the blocks is classified as text block, the whole image is recognized55
as a text image, otherwise a non-text image. Since blocks have various sizes and
positions so the proposed block level classification scheme allows us to detect
text at multiple scales and locations. Moreover, as a by-product, the proposed
MSP-Net predicts coarse locations and scales of text.
MSP-Net can be evaluated and trained efficiently. During testing, all blocks60
of an image are classified simultaneously in a single network forward propaga-
tion. Plus our optimized GPU implementation, the proposed network classifies
text/non-text images very efficiently. MSP-Net is end-to-end trainable, because
every layer of it can back-propagate error differentials. It can be easily trained
using images and corresponding block-level annotations.65
The contributions of this paper are summarized as the following: (1) We
propose a new scheme for text/non-text image classification based on block-
level classification, rather than whole image-level classification; (2) We propose
a novel variant of CNN, called MSP-Net, which efficiently classifies text/non-
text images, and is robust to the large variations on scale, location and language70
type of scene text; (3) As a by-product, we show that MSP-Net is also capable
of coarsely localizing scene text.
The rest of this paper is organized as followed. In Sec. 2 we review related
work. In Sec. 3, we describe and explain the architecture of MSP-Net. Ex-
perimental evaluations, comparisons with other methods, and discussions are75
presented in Sec. 4. Sec. 5 concludes our work.
2. Related work
Scene text reading. Scene text reading has been extensively studied in recent
years. Scene text detection and scene text recognition are two major topics in
this area. Most of the previous works focus on scene text detection and recogni-80
tion [4, 5, 7, 16, 17, 18]. As mentioned, text/non-text image classification can be
handled by a scene text detection algorithm. Epshtein et al. in [19] utilized the
stroke width transform to seek candidate character components. Neumann and
Matas in [20] extracted maximally stable extremal regions (MSERs) as candi-
date character regions to set up a novel and robust pipeline for text localization85
in real-world images. Different from the use of single character or stroke, Zhang
et al. exploited the symmetry property of character groups to directly extract
text-line candidates. However, most of them designed for precise localizing
text, which requires a lot of time to search and filter text/character candidates.
Whereas, text/non-text image classification aims at finding if a natural image90
contains text or not.
Image classification. In term of the essence, text image discrimination is a sub
task of image classification. The existing methods can be summarized into three
categories: feature encoding based methods, deep learning based methods, and
hybrid methods. The framework of Bag of Words (BoW) is a typical feature95
encoding based method. The local descriptors such as HOG [21], SIFT [22],
LBP [23], etc. of regions of interesting (ROIs) are extracted, and aggregated
by some feature encoding methods such as vector of locally aggregated descrip-
tors (VLAD) [24], locality-constrained linear coding (LLC) [25]. After then,
one image can be represented by a compact and discriminative vector, which100
are effective in image classification or retrieval. Recently, convolutional neu-
ral networks have achieved high performance of image classification. Thanks
to the CNN equipped with many convolutional layers, rectified units, sampling
layers, fully-connected layers,etc., the network can learn features and do im-
age classification in an end-to-end manner. The learned CNN features have105
demonstrated the effectiveness and robustness for image classification [26], ob-
ject detection [27], contour detection [28], etc. However, most of existing CNN
models require a fixed-size input image. He et al. [29] proposed SPP-net model
to generate a fixed-length representation regardless of image size/scale. In our
approach, we also take the advantage of spatial pyramid pooling to generate110
fixed-length representations for image blocks.
Text/non-text image classification. There are several works that address the
problem of text image discrimination in document images or video data, but
most of them aren’t suitable for natural images. In [30], Alessi et al. proposed a
method to detect the potential text blocks of document image and set a thresh-115
old value to distinguish text and non-text documents. Vidya et al. [31] proposed
a system to classify the text and non-text regions in handwritten documents,
which can’t deal with natural images either. To our knowledge, our previous
work [32] first proposed a suitable method that is the combination of three ma-
ture techniques including: MSERs, BoW, and CNN for text/non-text image120
classification. We also released a large dataset which can be a benchmark for
evaluating algorithms of text/non-text image classification. Another important
related work is the method proposed in [10], Shivakumara et al. first proposed
a method for video text frame classification based on fixed-size block partition.
The text block can indicate the coarse position of text. Inspired by this idea,125
our work proposes multi-scale spatial partition for natural text/non-text image
classification, due to the large variation of text scale and location in natural
scenes. Unlike the simple features adopted in [10], we adopt the convolutional
neural network to make the block-level prediction in a end-to-end manner by
moving the multi-scale spatial partition operation from image space to feature130
map. The multi-scale spatial partition plays the same role of ROI layer designed
in fast R-CNN [33], which can extract the CNN features for each region in an
efficient way. Furthermore, one image block classified as text block should con-
sider the scale and area together in our method, so that the text block in our
method can also predict the position and scale of text at a coarse level.135
3. The Proposed Methodology
3.1. Overview
spatial partition
feature maps
image-level feature
non-text blocks
text blocks
Figure 2: The overall architecture of MSP-Net.
As introduced in Sec. 1, our starting point is to classify text/non-text image
through the examining images at a block level. However, different from the
hand-crafted feature used for pre-partition image blocks in [10], our method140
combines spatial partition, feature extraction and text/non-text block classi-
fication into a single network (MSP-Net). The MSP-Net consists of 4 major
parts: image-level feature generation, multi-scale spatial partition, block-level
representation generation and text/non-text block classification sub-network.
The overall structure of MSP-Net is illustrated in Fig. 2, which only requires145
the whole image as an input and examines all the image blocks in an end-to-end
Given an input image, the network outputs block-level classification results
in a single forward propagation. Inside the network, first, an image is fed into
the convolutional layers, whose structure is derived from the VGG-16 CNN150
structure [26], to generate a hierarchy of feature maps. Feature maps are then
upsampled to the same size by deconvolutional layers, and concatenated in
depth, resulting in a representation that comprises equally sized feature maps.
Next, the maps are spatially partitioned into blocks of different sizes. The
adaptive max-pooling layer that equals to a spatial pyramid pooling layer [29]155
with only one pyramid level is applied to each block, producing feature vectors of
the same length. Following the pooling, feature vector for each block is fed into
the fully-connected layers which make the binary classification for that block.
The final classification of the whole image is the logical OR of the individual
block classification, i.e., as long as one block is classified as containing text, the160
image is considered text image, otherwise non-text image.
3.2. Image-level feature generation
Recently, feature maps from different convolution layers are combined to
make pixel-level prediction tasks successfully [34, 35, 36], as they carry rich
and hierarchical information. When implementing, all images are scaled to165
have a fixed height (500 pixels in our case), keeping their aspect ratios. The
feature generation part of MSP-Net consists of five convolutional layers that are
derived from the VGG-16 model [26], which has achieved superior performance
on image classification. Given the scaled input images, the convolutional layers
produce a hierarchy of feature maps, where the map sizes produced by different170
layers vary. Three deconvolutional layers are respectively connected to the third,
fourth and fifth convolutional layers (abbreviated as conv-3, conv-4, and conv-
5). Via deconvolution, the maps are upsampled to the same size. The feature
representation is then the concatenation in depth of these upsampled maps,
which is a hierarchical representation of the whole image.175
In a CNN, each convolutional layer has a particular receptive field size [37],
indicating the size of image region which every node on the feature maps is path-
connected to. Smaller receptive field sizes lead to finer feature granularity, while
larger sizes lead to coarser granularity. In our network settings, the receptive
field sizes of conv-3 is 40, which favors lower-level and local features. For conv-180
5, the size is 192, which enables it to describe higher-level global context. As
shown in Fig. 3, feature maps (which are upsampled) of conv-3 have higher
sensitivities to text strokes and edges, while feature maps of conv-4 and conv-5
favor the whole text regions.
(a) (b) (c) (d)
Figure 3: Feature maps of different layers. (a) is an input image, (b), (c) and (d) are feature
maps randomly selected from conv-3, conv-4 and conv-5, respectively.
The deconvolutional layers perform strided convolution on feature maps [35].185
They upsample input maps with ratios that are roughly the deconvolution
strides. With proper strides, we make output feature maps to have identical
width and height, so that they can be concatenated in depth.
3.3. Multi-scale spatial partition
Similar to the ROI pooling layer designed for fast feature extraction for
each proposal in Fast R-CNN [33], we move the operation of multi-scale spatial
partition from image-level space to feature-level space, in order to efficiently
obtain the features of each image block. In the partition step, the generated
feature maps are spatially partitioned into blocks with respect to several block
sizes. We use block sizes of w
N, where w, h are the width and height of the
input feature maps, and Nis an integer. Each block size uniformly partitions the
maps into N2equally sized blocks. Mathematically, the partition is formulated
Fij (x, y) = F(x+iw
N, y +jh
0x < w
0y < h
where F(x, y) denotes the generated feature maps, Fij denotes the block at row190
j, column i(i, j are indexs of row and column, both of them start from 0 to
Following [29, 33], each block on the feature maps is associated to a region
on the input image:
Iij (x, y) = I(x+iW
N, y +jH
0x < W
0y < H
where I(x, y) is an input image whose size is W×H. We let the feature block
describe its corresponding image region. Although this results in redundant
description, since the receptive field for the feature block would be larger than195
the region we define, this simplifies our formulations, and works well in prac-
tice [29, 33]. Furthermore, we perform multi-scale spatial partition by choosing
different values for N(e.g., 1, 3, 5 and 7), resulting in feature blocks of different
sizes. The feature blocks describe local image regions of different sizes, and they
are all used for the following adaptive pooling.200
In a neural network, all operations need to back propagate error differentials.
The back-propagation of the multi-scale spatial partition operation is formulated
δL/δ F (x, y) = X
δL/δ F ij
Nc(xiN, y jN),
0y < h,
where Ldenotes the loss, the back-propagation on multi-scale spatial partition
operation is the sum of back-propagation of each feature block δL/δF ij .
3.4. Block-level representation and classification
Since multiple scale values are used in multi-scale spatial partition ( we use
4 scales to partition feature maps into 1 ×1, 3 ×3, 5 ×5 and 7 ×7 feature205
blocks, respectively.), the output feature blocks represent corresponding image
blocks are of different sizes, which are illustrated in Fig. 2. Hence, we normalize
the representation of each image block into the same size for feeding it into
the classification sub-network. In order to generate fixed-length feature repre-
sentation, an adaptive max-pooling layer is adopted. As one scale of spatial210
partition illustrated in Fig. 4, a block is equally divided into Ns×Nssub-blocks
(Ns×Nsdenotes the bock number partitioned under the s-th scale, s= 1 and
Ns×Ns= 3 ×3 here), in a similar way which an image is divided into blocks.
Then, max-pooling operation is applied to every block to generate a feature vec-
tor, whose length is Nmap, which is the depth of the feature map. Last, feature215
vectors generated from all blocks are concatenated into one block, whose length
is then N2
The spatial partition in a block is similar to the partition on feature maps,
described in Sec. 3.3. However, the purpose of dividing blocks into sub-blocks
is to capture the spatial relationships within a block, in order to improve the220
discrimination power of the resulting block-level representation. Essentially,
the sub-network that generates block-level representation is a special case of
the spatial pyramid pooling layer used in SPP-Net [29]. The spatial pyramid
pooling layer consists of several pyramid level of pooling layers, where each
pooling layer is adaptive layer that outputs fixed-size feature by divided the225
feature map into fixed-size bins. In fact, our spatial partition operation is equal
to 1 pyramid level of spatial pyramid pooling layer whose partition bin number
is Ns×Ns.
After feature extraction for all blocks of an image, we classify the blocks
using a single classification sub-network. The classification sub-network is a230
part of MSP-Net, which consists of three fully-connected layers. Since fixed-
length representation of each image block is generated by adaptive max-pooling,
all feature vectors can be fed into the classification sub-network in the form of
batch processing to make the text/non-text block classification.
Besides, the numbers of dimensions for all block descriptors are the same, so235
the classification sub-network accepts blocks of arbitrary sizes. Recall that other
parts of the network, namely convolutional layers, deconvolutional layers, and
spatial partition layers, also accepts arbitrarily-sized input maps. Consequently,
MSP-Net classifies input images of arbitrary sizes. This property allows us
fixed-length representation
(36× 𝑁𝑚𝑎𝑝-d)
spatial pyramid pooling
(only one level: 6× 6 )
3×3 feature blocks
one feature block
(arbitrary size)
Figure 4: Block-level feature generation.
to directly feed original images into the network during testing, without any240
cropping or resizing that may cause loss of information.
3.5. Network training
Ground truth. The image blocks that are defined as text blocks must meet two
constraint conditions: text area and scale. We use r1 denotes the text occupy
ratio in one image block, and the height ratio of text lines to the image block245
represented as r2. In our experiments, the value of r1 must be over 0.05, as well
as r2 must be over 0.5.As the dataset not only provides the image-level label
but bounding boxes of text lines, we can easily infer the ground truth of all
image blocks. As one example illustrated in Fig. 5, the yellow bounding boxes
in Fig. 5(b) are the ground truth of text lines, which indicate the text area and250
scale (or height) of text lines. Therefore, each image block generated by multi-
scale spatial partition in Fig. 5(c)Fig. 5(f) is defined as positive if it meets two
constraints above, otherwise as negative. Besides, if an image block is classified
as text block, it not only means the whole image should be considered as text
image, but also indicates the coarse position and scale of text.255
Loss definition. Due to the binary class output of MSP-Net, we use the cross-
entropy loss function as the objective function. Suppose a training image I
(a) (b) (c)
(d) (e) (f)
Figure 5: Ground truth of image blocks with different scales. (a) is a natural text image,
yellow bounding boxes in (b) show the text lines. Image is partitioned with multiple scales of
1×1, 3 ×3, 5 ×5, 7 ×7 in (c), (d), (e), and (f), respectively. The white blocks mean positive
and the black blocks are negative.
is partitioned with Nimage blocks, whose labels are denoted by {li}N
i. The
objective is to minimize the sum cross-entropy loss of all image blocks:
(lilog pi+ (1 li) log(1 pi)),(4)
where piis the probability of i-th image block classified as text block,liis the
label of i-th image block.
We use the VGG-16 model which is pre-trained on ImageNet [15] to initial-
ize the 5 convolutional stages (first 13 convolutional layers) of MSP-Net. Then,
stochastic gradient descent (SGD) is adopted to jointly optimize whole param-
eters by the back-propagation algorithm. Since the number of text blocks is
much smaller than the one of non-text blocks, we use the class-balancing weight
as a simple way to offset this imbalance between text/non-text block. Thus, we
replace the equation (4) with the following formulation:
(λlilog pi+ (1 λ)(1 li) log(1 pi)),(5)
where λdenotes the class-balancing weight, whose value is 2/3 in the training
4. Experiments260
In this section, we first evaluate the proposed method on several public
benchmarks including the TextDis benchmark [32], the ICDAR2003 dataset [38]
and Hua’s dataset [39]. Then we compare our method with some existing meth-
ods, which are either text/non-text image classification methods or general im-
age classification methods. Last, in the discussion part, we evaluate the effects265
of some parameters in our design.
4.1. Datasets
TextDis benchmark. This dataset is introduced in [32], which contains 7302
text images and 8000 non-text images. The benchmark randomly selects 2000
images for each class to build the testing dataset, and the remaining images are270
used for training. To our knowledge, this dataset is the first dataset for the
discrimination of text and non-text natural image. Due to the large variation
in the fonts, scales, colors, languages and orientations of text in the image, this
dataset is quite challenging. Precision, recall and F-Measure are used as the
evaluation protocol for measuring the results of different algorithms.275
ICDAR2003 dataset. 251 camera images are collected and released for evaluat-
ing scene text detection methods. Since all images are taken from natural scene,
there is still large variation in the fonts, scales and colors of text. The most sig-
nificant differences from TextDis lie in that the language of text is English only
and the orientation of text is horizontal or nearly horizontal.280
Hua’s dataset. This dataset is a small video text detection benchmark, which
contains 42 text frames and 3 non-text frames. Different from natural images,
text appearing in text frames usually has regular formats including fonts, scales
and positions.
4.2. Implementation details285
Table 1: The details of MSP-Net. Each convolutional stage has 2 or 3 convolutional layers.
’k’,’s’ and ’p’ mean kernel size, stride, and padding size in convolutional layers. And ’ws’
means the window size of pooling layer.
Layers Configurations
conv-1 2×{#map:64, k:3×3, s:1, p:1}
maxpooling ws:2 ×2, s:2
conv-2 2×{#map:128, k:3×3, s:1, p:1}
maxpooling ws:2 ×2, s:2
conv-3 3×{#map:256, k:3×3, s:1, p:1}
maxpooling ws:2 ×2, s:2
conv-4 3×{#map:512, k:3×3, s:1, p:1}
maxpooling ws:2 ×2, s:2
conv-5 3×{#map:512, k:3×3, s:1, p:1}
deconv-3 #map:128, k:1 ×1, s:1
deconv-4 #map:256, k:4 ×4, s:2
deconv-5 #map:256, k:8 ×8, s:4
muti-scale spatial partition #bin:{1×1, 3 ×3, 5 ×5, 7 ×7}
adaptive max-pooling #bin:6 ×6
fc-1 #unit:4096
fc-2 #unit:4096
output #uint:2
Architecture details. The details of our proposed network (MSP-Net) are listed
in Table 1. The first 5 convolutional stages are derived from VGG-16 model,
feature maps from conv-3, conv-4 and conv-5 are followed with up-sampling
layers which are replaced by deconvolutional layers with different strides to make
the feature maps have the same size. The multi-scale spatial partition with 4290
scales ( e.g. 1 ×1, 3 ×3, 5 ×5, 7 ×7) are adopted in the feature map space
to efficiently generate features for 84 image blocks. After the spatial pyramid
pooling layer with only one level (i.e. 6 ×6), the feature size of each block is
(128 + 256 + 256) ×6×6. Finally, 84 feature blocks together form a team input
to the classification sub-network for the final text/non-text block classification.295
The classification sub-network consist of three fully-connected layers. Naturally,
if at least one block is classified as text block, the whole image is treated as text
Data preparation. We apply rotation and flipping operations to each training
image, and randomly crop 10 image regions with the same aspect ratio for data300
argumentation. After that, all training image regions are resized to fixed height
(500 pixels). Since 4 different scales are used in the layer of multi-scale spatial
partition, the heights of image blocks in 4 partition scales correspond to 500,
167, 100 and 71. Due to r2(the minimal height ratio of text line in image block)
is set to 0.5, one image block regarded as text block must meet the minimal305
height values: 250, 83, 50, and 10 for 4 partition scales.
Training details. We use stochastic gradient descent( SGD ) to fine-tune the
MSP-Net whose details are listed in 1 with following parameters: mini-batch
size is 1 (due to multi-scale spatial partition, the number of image blocks is 84),
learning rate is 1e-6 (divided 10 after each 50K iterations), momentum value310
is 0.9, and weight decay is 0.0002. Training takes about 10 hours for a single
GPU (NVIDIA GTX TitanX). In testing phase, an input image is also resized
to the fixed height and fed into the trained network to output 84 block-level
prediction results. Furthermore, the MSP-Net is trained on TextDis benchmark,
then tested on all datasets.315
4.3. Comparison methods
Locality-constrained Linear Coding (LLC). LLC [25] is a useful coding method
for image classification. In our paper, we extract dense sift features of 3 different
scales (e.g.,8 ×8, 16 ×16, 24 ×24), and the size of codebook clustered by k-
means is set to 2048. Besides, the spatial pyramid matching is replaced by320
global max-pooling, which still achieves a comparable result.
Spatial Pyramid Pooling Network (SPP-Net). The spatial pyramid pooling layer
proposed in [29] can generate fixed-size and hierarchical features for image or
region in arbitrary sizes, which achieves a quite competitive performance on
object detection and recognition. In our comparison experiments, the SPP-Net325
adopts the same convolutional stages as our proposed method, and the pyramid
levels are in 3 scales (e.g., 1 ×1, 3 ×3, 5 ×5). However, the output of SPP-Net
is the image-level classification, which is different from our method.
CNN Coding. In our previous work [32], we proposed a method that combines
maximally stable extremal region (MSER), convolutional neural network (CNN)330
and bag of words (BoW) for text image discrimination. This work utilizes the
MSER to extract text candidates and feeds them into a trained CNN model to
generate visual features, then all features are aggregated by BoW to obtain the
final representation for natural image. All the same parameters in [32] are used
for this comparison experiment.335
In the above methods for the comparison, LLC and SPP-Net only use the
information of image label, while the method of CNN Coding uses both image
label and text-line bounding box information to classify an image. Therefore,
the comparison between MSP-Net and CNN coding is more fair and represen-
4.4. Experiments results
4.4.1. Experiments on TextDis benchmark
In Table 2, the quantitative classification results of different methods on
TextDis benchmark are listed. The proposed method (MSP-Net) outperforms
CNN Coding by 3.9% in precision, 5.1% in recall and 4.5% in F-measure. And345
the speed of MSP-Net is more than 3 times faster than CNN Coding. The com-
parison results between MSP-Net and SPP-Net show that it is hard to achieve
satisfied performance, if we directly use the existing framework of convolutional
Table 2: The results of different comparison methods. The metrics including precision, recall,
F-measure and time cost are presented.
Methods Precision Recall F-Measure Time Cost
LLC 0.839 0.774 0.805 0.30s
SPP-Net 0.841 0.839 0.840 0.16s
CNN Coding 0.898 0.903 0.901 0.46s
MSP-Net 0.937 0.954 0.946 0.13s
network to do text/non-text image classification. In order to intuitively illus-
trate the advanced performance of MSP-Net, we also plot the precision-recall350
curves of different methods. Note that the MSP-Net can only output the confi-
dence of image block identified as text block, so we use the maximum confidence
value of all image blocks to approximate the score of the whole image that is
classified as a text image. The curve of MSP-Net in Fig. 6 shows that our
method keeps rather high precision even at the range of high recall.355
In addition, an important advantage of our proposed method is that text
blocks can indicate the coarse position and scale of text appeared in text image.
In order to better display this advantage, we keep all pixels of text blocks and
remove all non-text blocks. As shown in Fig. 7, text images are successfully
classified and their candidate text blocks highlighted with red bounding boxes360
in the second row are kept. Meanwhile, the majority of text in text images is
kept, and the scale (or height) of text line is comparable to the height of block
which it belongs to. Different from other comparison methods which obtain
only the image-level confidence of text image, our method can provide richer
and more helpful information for scene text reading system.365
4.4.2. Experiments on ICDAR2003 dataset
ICDAR2003 dataset is a publicly available scene text dataset whose text is
focused. We test our proposed method on ICDAR2003 to show that it works
well on focused text images. In order to acquire intuitive and fair comparison
results of the methods proposed in [10, 11], we use the classification rate and370
the average processing time (APT) as the metrics.
Figure 6: The precision-recall curves of comparison methods.
The results of different methods are list in Table. 3, which show that our
method outperforms the video text frame classification methods [10, 11]. What’s
more, the average processing time of MSP-Net is much less. Some examples of
ICDAR2003 dataset are shown in Fig. 8.375
4.4.3. Experiments on Hua’s dataset
To discuss the generalization of our proposed method in video frames, we
test it on Hua’s dataset. The same metrics used in Sec. 4.4.2 are utilized to
evaluate the performances of different methods. The results in Table. 4 show
that our method has obtained the highest classification results. What’s more,380
the average processing time (APT) for each frame is quite faster than the other
two methods [11, 10] which are specially designed for text frame classification.
Figure 7: Classification results of TextDis benchmark. (a) are some samples of text images
from TextDis benchmark, red bounding boxes in (b) mean the text blocks detected by MSP-
Net, (c) keeps all pixels of text blocks.
Table 3: Classification rates of proposed methods and existing methods on ICDAR2003.
Methods Text(%) Error(%) APT
Proposed method 89.2 10.8 0.132s
Shivakumara et al. [11] 80.97 19.03 1.23s
Shivakumara et al. [10] 81.12 18.88 N.A
In Fig. 9, we show some results of our method tested on Hua’s dataset. Most
text in Hua’s dataset is in the form of caption, which is easily captured, for
example video frames at the first, second and third column of Fig. 9. Besides,385
some scene text in video frames can also be well captured by our proposed
method, like video frames in the fourth and fifth columns of Fig. 9.
4.5. Discussion
4.5.1. Effect of feature combination
In our proposed method, features from different convolutional layers are390
concatenated after up-sampling to generate richer and more hierarchical fea-
tures. In order to discuss the effect of different groups of feature concatenation,
Figure 8: Classification results of ICDAR2003 dataset. (a) are some samples of text images
from ICDAR2003 dataset, red bounding boxes in (b) mean the text blocks detected by MSP-
Net, (c) keeps all pixels of text blocks.
we adjust the feature maps from different convolutonal layers and keep other
settings of the network. Table 5 list three settings of feature concatenation
and performance on the TextDis benchmark. From the listed results, the com-395
parison between Variant-1 and Variant-2 (or MSP-Net) also demonstrates that
different feature maps that represent information with different levels can be
concatenated to form rich and hierarchical representation for text/non-text im-
age. More feature maps from different convolutional stages are concatenated,
the final performance would be enhanced. Since the size of feature map at400
conv-1 and conv-2 stages is large, which would need more memory and consum-
ing time for feature concatenation, we don’t use feature maps from these two
convolutional stages.
4.5.2. Effect of multiple scale for spatial partition
Since the large variance of natural text, especially the scale and area, we405
demonstrate the importance of multi-scale spatial partition through the com-
Table 4: Classification rates of proposed methods and existing methods on Hua’s dataset.
Methods Text(%) Non-
Proposed method 100 100 0.127s
Shivakumara et al. [11] 97.62 100 1.05s
Shivakumara et al. [10] 75.54 24.46 2.04s
Figure 9: Classification results of Hua’s dataset. (a) are some samples of text images from
Hua’s dataset, red bounding boxes in (b) mean the text blocks detected by MSP-Net, (c)
keeps all pixels of text blocks.
parison experiments with several groups of single-layer spatial partition. In
practice, we only change the layer of multi-scale spatial partition with differ-
ent numbers and scales, keeping the same configuration of other layers. In
Tab. 6, the result of multi-scale spatial partition outperforms any single spatial410
partition method. Although the result of single-layer with 7×7 achieves consid-
erable results, the multi-scale partition has obvious improvement. According to
the comparison results, we can demonstrate that convolutional neural network
can learn richer and more discriminative features for text block discrimination
if the range of text scale is proper.415
Table 5: Results of different settings of feature combination. Variant-1 only uses the feature
maps from 5-th convolutional stages and Variant-2 combines the feature maps from 4-th and
5-th stages.
Variants Settings Precision Recall F-Measure Time Cost
Variant-1 conv-5 0.915 0.890 0.905 0.106s
Variant-2 conv4 + conv5 0.924 0.945 0.936 0.118s
MSP-Net conv-3 + conv-4 + conv-5 0.937 0.954 0.946 0.130s
Table 6: Effect of multiple scale for spatial partition.
Scale Precision Recall F-Measure
1×1 0.825 0.819 0.822
3×3 0.870 0.864 0.867
5×5 0.892 0.921 0.906
7×7 0.931 0.914 0.922
1×1,3×3,5×5,7×7 0.937 0.954 0.946
4.5.3. Comparing with text detection methods
Table 7: Classifying text/non-text images on TextDis benchmark with different text detection
Methods Precision Recall F-
MSP-Net 0.937 0.954 0.946
Zhang et al. [40] 0.754 0.979 0.851
Yao et al. [6] 0.808 0.902 0.853
Neumann et al. [20] 0.525 0.984 0.685
In this section, we compare MSP-Net with some existing natural text detec-
tion methods on classifying text/non-text image, which shows the effectiveness
and efficiency of our proposed method. Similar with the classification mech-
anism of MSP-Net, text detection methods classify one natural image as text420
image as long as one text line on it is detected. The results of different text
detection methods on TextDis benchmark are listed in Tab. 7. The MSP-Net
obtain the highest accuracy as well as the least time.
Table 8: Time cost between Only Text Detection and MSP-Net +Text Detection on
TextDis benchmark.
Methods Only Text
MSP-Net +
Text Detec-
Zhang et al. [40] 2.10s 0.85s
Yao et al. [6] 5.00s 2.10s
Neumann et al. [20] 0.94s 0.46s
Besides, we find a interesting phenomenan that the time cost of text detec-
tion would be largely decreased if we use the MSP-Net to eliminate the non-text425
images before. In the Tab. 8, we find the speeds of text detection methods on
TextDis benchmark are about more than doubled.
4.6. Limitations of the proposed method
While our proposed method outperforms other compared methods, there still
exists some failure cases. Text in difficult natural conditions would get wrong430
classification using our proposed method. For example, text in Fig. 10(a) is in
the condition of low illumination, while text in Fig. 10(b) are exposed. And some
regular curves, bricks or windows in Fig. 10(c),Fig. 10(d) are similar to text,
and would make false positive results. Due to the rigid spatial partition , the
majority of text is kept after text/non-text block classification, but sometimes435
the remaining text is fragile if some text blocks are misclassified, shown in
Fig. 10(e)(f). In other way, the proposed method is based on the framework of
convolutional neural network, and therefore its time cost is limited to GPU.
5. Conclusion
In this paper, we have proposed a novel architecture of convolutional neural440
network (named MSP-Net) for text/non-text image classification. The MSP-
Net takes input as a whole image and outputs block-level classification results in
an end-to-end manner. The results on several datasets have demonstrated the
(a) (b) (c) (d) (e) (f)
Figure 10: Some failure cases. (a),(b) are text images in difficult conditions. Some curves
or objects in (c),(d) are similar to text. Some true text blocks in (e) and (f) are eliminated,
which make the remaining text is fragile.
robustness and effectiveness of our proposed method. Besides, one image block
classified as text block can also coarsely indicate the scale and position of text,445
which is helpful to scene text reading. The combination of text/non-text image
classification with scene text reading system for mining scene text semantics
from the large scale images/videos on the Internet is worthy of exploration in
our future work.
6. Acknowledgements450
This work was supported by National Natural Science Foundation of China
(NSFC) No. 61222308 and No. 61573160, and in part by Program for New
Century Excellent Talents in University (No. NCET-12-0217).
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Conference Paper
Can a large convolutional neural network trained for whole-image classification on ImageNet be coaxed into detecting objects in PASCAL? We show that the answer is yes, and that the resulting system is simple, scalable, and boosts mean average precision, relative to the venerable deformable part model, by more than 40% (achieving a final mAP of 48% on VOC 2007). Our framework combines powerful computer vision techniques for generating bottom-up region proposals with recent advances in learning high-capacity convolutional neural networks. We call the resulting system R-CNN: Regions with CNN features. The same framework is also competitive with state-of-the-art semantic segmentation methods, demonstrating its flexibility. Beyond these results, we execute a battery of experiments that provide insight into what the network learns to represent, revealing a rich hierarchy of discriminative and often semantically meaningful features.
This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.
In this paper, we are concerned with the problem of automatic scene text recognition, which involves localizing and reading characters in natural images. We investigate this problem from the perspective of representation and propose a novel multi-scale representation, which leads to accurate, robust character identification and recognition. This representation consists of a set of mid-level primitives, termed strokelets, which capture the underlying substructures of characters at different granularities. The Strokelets possess four distinctive advantages: 1) usability: automatically learned from character level annotations; 2) robustness: insensitive to interference factors; 3) generality: applicable to variant languages; and 4) expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of the strokelets and demonstrate the effectiveness of the text recognition algorithm built upon the strokelets. Moreover, we show the method to incorporate the strokelets to improve the performance of scene text detection.