A Blended Attention-CTC Network Architecture for Amharic
Text-image Recognition
Birhanu Hailu Belay
, Tewodros Habtegebrial
, Marcus Liwicki
, Gebeyehu Belay
and Didier Stricker
Technical University of Kaiserslautern, Kaiserslautern, Germany
Lulea University of Technology, Lulea, Sweden
Bahir Dar Institute of Technology, Bahir Dar, Ethiopia
DFKI, Augmented Vision Department, Kaiserslautern, Germany
Amharic Script, Blended Attention-CTC, BLSTM, CNN, Encoder-decoder, Network Architecture, OCR,
Pattern Recognition.
In this paper, we propose a blended Attention-Connectionist Temporal Classification (CTC) network archi-
tecture for a unique script, Amharic, text-image recognition. Amharic is an indigenous Ethiopic script that
uses 34 consonant characters with their 7 vowel variants of each and 50 labialized characters which are de-
rived, with a small change, from the 34 consonant characters. The change involves modifying the structure
of these characters by adding a straight line, or shortening and/or elongating one of its main legs including
the addition of small diacritics to the right, left, top or bottom of the character. Such a small change affects
orthographic identities of character and results in shape similarly among characters which are interesting, but
challenging task, for OCR research. Motivated with the recent success of attention mechanism on neural ma-
chine translation tasks, we propose an attention-based CTC approach which is designed by blending attention
mechanism directly within the CTC network. The proposed model consists of an encoder module, attention
module and transcription module in a unified framework. The efficacy of the proposed model on the Amharic
language shows that attention mechanism allows learning powerful representations by integrating information
from different time steps. Our method outperforms state-of-the-art methods and achieves 1.04% and 0.93% of
the character error rate on ADOCR test datasets.
Amharic is an official working language of the Fed-
eral Democratic Republic of Ethiopia and it is the
second most widely spoken Semitic language in the
world next to Arabic. Amharic is spoken by more
than 100 million people in the country and it is
also widely spoken in different countries like Eritrea,
USA, Israel, Somalia and Djibouti (Meshesha and
Jawahar, 2007; Amh, ; Mekuria and Mekuria, 2018).
In Amharic script, there are about 317 differ-
ent alphabets including 238 core characters, 50 labi-
alaize characters, 9 punctuation marks and 20 numer-
als which are written and read, like English, from left
to right (Meshesha and Jawahar, 2007; Belay et al.,
2019a; Belay et al., 2019b). There are multiple doc-
uments, containing religious and academic contents,
written in Amharic script dated back from 12
tury (Meyer, 2006). Since then, these documents are
stored in different places such as Ethiopian Orthodox
Tewahdo Churches, public and academic libraries in
the form of hardcover books. With a digitization cam-
paign, many of these manuscripts are collected from
different sources. However, they are still preserved
in a manual catalog and/or scanned copies of them in
Microfilm format (Wion, 2006).
The shape and structural formation of sample ba-
sic Amharic characters with their unique features are
depicted in Figure 2.
Numerous works, in area of Optical Character
Recognition (OCR) and Document Image Analysis
(DIA), have been done and widely used for decades
to digitize various historical and modern documents
(Breuel et al., 2013; Maitra et al., 2015; Mondal et al.,
2017; Martınek et al., 2020). Researchers achieved a
high recognition accuracy and most scripts now have
commercial off-the-shelf OCR applications. How-
ever, OCR often gives a better recognition result only
Belay, B., Habtegebrial, T., Liwicki, M., Belay, G. and Stricker, D.
A Blended Attention-CTC Network Architecture for Amharic Text-image Recognition.
DOI: 10.5220/0010284204350441
In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2021), pages 435-441
ISBN: 978-989-758-486-2
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Figure 1: Shape formation of sample basic Amharic char-
acters (Belay et al., 2020). Orders of consonant-vowel vari-
ants (34 × 7). Characters in the first column are consonants
and the others are derived variants. Vowels are derived by
adding diacritics and/or remove part of consonants and the
orthographic identities of each character vary across rows
as marked with the red color.
for a specific use cases, moreover there are multiple
indigenous scripts, like Amharic, which are underrep-
resented in the area of Natural Language Processing
(NLP) and DIA (Belay et al., 2019b).
Even though OCR research for Amharic script
started in 1997 (Alemu, 1997), it is still in its infancy
and it is still an open area of research. Since then,
attempts have been made to develop Amharic OCR
(Meshesha and Jawahar, 2007; Alemu, 1997; Cow-
ell and Hussain, 2003; Assabie and Bigun, 2009) us-
ing different statistical machine learning techniques.
Recently, following the success of deep learning,
other attempts are also made to develop a model for
Amharic OCR and achieved relatively promising re-
sults (Belay et al., 2019a; Belay et al., 2019b; Belay
et al., 2018; Reta et al., 2018; Gondere et al., 2019).
In literature, attempts to Amharic OCR neither
shown results on large dataset nor considering all pos-
sible characters used in Amharic writing system. Re-
cently publish work (Belay et al., 2019b), introduced
an Amharic OCR database called ADOCR. We took a
sample text-line image from ADOCR database whose
word formation and character arrangements in a sam-
ple word are illustrated in Figure 2.
Amharic characters
Corresponding English letters
Figure 2: Sample Amharic text-line image from the dataset.
A word marked by red box is composed of five individual
Amharic characters and the corresponding sounds of each
character is described with English letters using red color.
Convolutional and Recurrent networks have been
used in Amharic OCR. In this paper we aim to push
the limits of Amharic OCR models by introducing at-
tention mechanism. Therefore, in this paper, we pro-
pose an attention based CTC network called Blended
Attention CTC (BACTC) for Amharic text-line im-
age recognition. BACTC has a CNN-LSTM layers as
encoder followed by attention and CTC layers which
used to pick the only important features of encoded
inputs and transcription respectively.
The rest of the paper is organized as follows. Sec-
tion 2 talks about related works. Our proposed model
is presented in section 3, and section 4 presents the
detail of datasets. In the last two sections, experimen-
tal results and conclusions are presented respectively.
Previous research on Amharic OCR was focused on
the statistical machine learning techniques. Most of
these techniques are segmentation based and charac-
ter level OCR models (Meshesha and Jawahar, 2007;
Belay et al., 2019a; Cowell and Hussain, 2003; Belay
et al., 2018). The only exception work were Assabie
(Assabie and Bigun, 2009) who proposed a segmenta-
tion free OCR based on HMM model for offline hand-
written Amharic word recognition. Recently pub-
lished works (Addis et al., 2018) and (Belay et al.,
2019b) proposed a Bidirectional LSTM (BLSTM)
network architecture with CTC for Amharic text-line
image recognition. An end-to-end learning, that uses
CNN, LSTM and CTC in a unified framework (Belay
et al., 2020), is also proposed for Amharic OCR and
achieved a better recognition performance.
Following the first Amharic OCR research, which
was only able to recognize a character written with
Washera font and 12 point type, attempted by Worku
in 1997 (Alemu, 1997), other research works have
been made including typewritten (Teferi, 1999), ma-
chine printed (Meshesha and Jawahar, 2007; Be-
lay et al., 2018), Amharic document image recogni-
tion and retrieval (Meshesha, 2008), Ethiopic number
(Reta et al., 2018) and handwritten (Gondere et al.,
2019) recognition.
Based on recurrent neural network, several
OCR techniques have been studied and demon-
strated groundbreaking performances for multiple
Latin and Non-Latin scripts. BLSTM with CTC
for Amharic text-image recognition (Belay et al.,
2019b), Convolutional Recurrent Neural Network
(CRNN) for Japanese handwritten recognition (Ly
et al., 2017), segmentation free Chinese handwritten
text recognition (Messina and Louradour, 2015), a
ICPRAM 2021 - 10th International Conference on Pattern Recognition Applications and Methods
hybrid Convolutional-LSTM for text-image recogni-
tion (Breuel, 2017), Multidimensional LSTM for Chi-
nese handwritten recognition (Wu et al., 2017), com-
bined Connectionist Temporal Classification (CTC)
with Bidirectional LSTM for unconstrained online
handwriting recognition (Graves et al., 2008).
Attention based networks have been intensively
applied in the area of NLP tasks and came up with
successive results in neural machine translation (Bah-
danau et al., 2014; Luong et al., 2015; Ghader and
Monz, 2017) and speech recognition (Das et al., 2019;
Watanabe et al., 2017). The most works so far with
attention mechanism has focused on neural machine
translation. However, researchers have recently ap-
plied attention in different research areas. Therefore,
it becomes popular and a choice of many researchers
in the area of OCR.
Attention mechanism is now also widely applied
for recognizing handwritten texts (Poulos and Valle,
2017; Chowdhury and Vig, 2018), characters in the
wild (Lee and Osindero, 2016; Huang et al., 2019;
Huang et al., 2016), and handwritten mathematical
expression (Zhang et al., 2018; Li et al., 2020). At-
tention mechanism assists the network in learning the
correct alignment between the input image pixels and
the target characters. In addition, it improves the abil-
ity of the network in extracting the most relevant fea-
ture for each part of the output sequence. Inspired
by the success of attention in sequence to sequence
translation, we continue to focus on OCR tasks, and
we integrate the capability of attention mechanism in
the CTC network so as utilize the benefits from both
The proposed blended attention-CTC model, as
shown in Figure 3, consists of three modules. The
encoder module takes the input features x and maps
them to a higher-level feature representation h
). The attention module takes
the output features h
of the encoder module and
computes the context vector from each hidden fea-
tures. The output of the attention module, atten-
tion context information, is passed to the Soft-max
layer in order to produce a probability distribution,
,..., y
|x) over the given input sequence x. Fi-
nally, the probability distribution P goes to the CTC-
decoder for transcription.
The proposed method integrates attention mecha-
nism, LSTM and CTC layers. The intuition for the
use of the attention layer is to infer a more power-
Encoder-LSTM Encoder-LSTM
Input text-image
Output text
የአያቴን ታሪኮች ይዤላችሁ
Attention layer
Figure 3: The proposed blended attention-CTC model.
Alignment score (a
) of encoder hidden state (h
) at each
time-step is computed using a scoring function described
in (3), just by propagating the h
through fully con-
nected network (FC). Attention distribution, called atten-
tion weights (s
) are computed by running all alignment
scores over a soft-max (Soft-max-1) layer. To compute the
alignment vector , we multiply each h
with its corre-
sponding soft-maxed score (s
). Then, the sum of all align-
ment vectors produced the context vector (C
), which is
an aggregated information. Once the C
is obtained, it
passes through the second soft-max layer (Soft-max-2) for
probability distribution over the n possible characters in the
ground-truth (GT). The output of Soft-max-2 is a sequence
of T time steps of (n + 1) characters which is then decoded
using CTC-decoder.
ful hidden representation through a weighed a context
vector (C
). The attention based weighting offers a
powerful way to aggregate inputs from different time
steps. The weighted context vector is computed using
Equation (1). The training objective of the proposed
model follows the same CTC training objective ex-
plained in (Graves et al., 2008).
where the s
is an attention weight of each annotation
computed by soft-maxing its corresponding at-
tention score using Equation (2),
where a
is the alignment score of h
at each time
A Blended Attention-CTC Network Architecture for Amharic Text-image Recognition
step t and it can be computed using Equation (3).
= f (h
),for i = 1, ...,T
The function f in Equation (3) is a feed-forward neu-
ral network with tanh function. The intuition of this
scoring function is to let the model to learn the align-
ment weights together with the translation while train-
ing the whole model layers.
During training the blended attention-CTC model,
a CTC loss function (l
) is used to train the net-
work from end-to-end. For training data D, the func-
tion l
can be defined as in Equation (4).
= log
where x = (x
,...., x
) is the input sequence with
length T, and z = (z
,..., z
) is the corresponding
output sequence in ground-truth for C < T in every
pair of x and z. The P(z/x) is computed by multi-
plying the probability of labels along the path π that
contains output label over all time steps t as shown in
Equation (5).
p(π|x) =
,t|x) (5)
where t is the time step and π
is the label of path π at
A target label in path π is obtained by mapping
reduction function B, using the example explained in
(Belay et al., 2019b), that convert a sequence of Soft-
max output for each frame to a label sequence by
removing repeated labels and blank tokens from the
sequences of character (C) with the highest score (i)
generated using Equation (6).
To the best of our knowledge, ADOCR (Belay
et al., 2019b) database is the only publicly available
Amharic OCR dataset with the benchmark experi-
mental results. Therefore, to train and evaluate the
proposed model, we use the ADOCR database intro-
duced by (Belay et al., 2019b) and which is freely
available at http://www.dfki.uni-kl.de/
The original Amharic OCR database composed of
337,337 Amharic text-line images, each with multi-
ple word instance collected from different sources. In
this database there are about 40,929 printed text-line
images written with power Geez font 197,484 and
98,924 text-line images synthetically generated with
Power Geez and Visual Geez fonts respectively.
In addition, the ADOCR database contains 280
unique Amharic characters and punctuation marks
which are mutually exist in both the training and test
samples. All images a 48 by 128 pixels gray-scale,
and the maximum string length of the ground-truth
text is 32 characters.
Then the images are normalized into 32 by 128
pixels as stated in (Belay et al., 2020). and sample
Amharic text-line images from ADOCR database are
given in Figure 4.
Figure 4: Sample Amharic text-line images from ADOCR
database: a) Printed text-line images written by power Geez
font type. (b) Synthetic text-line images generated with
Power Geez font type. (c) Synthetic text-line images gener-
ated with the Visual Geez font.
We train and test our model on the ADOCR database
which contains 318,706 training and 18,631 test sam-
ples. Training details and experimental results are
presented below.
5.1 Training
Our model is trained with the ADOCR database.
Similar to (Belay et al., 2020), images are scaled
to 32 by 128 pixels so as to minimize computa-
tions. Since there is no explicitly stated validation
data, in ADOCR dataset, we randomly selected 7%
of the training samples for validation samples. In
this study, we first implement and train an attention
based encoder-decoder network proposed by Bah-
danau (Bahdanau et al., 2014) and then the blended
attention-CTC network is formulated. In the later
model, the main contribution of this paper, we di-
rectly taking the advantage of attention mechanism
and CTC network as an integrated framework and
trained in an end-to-end fashion.
When training both models, we use two bi-
directional LSTM, each with 128 hidden units and
ICPRAM 2021 - 10th International Conference on Pattern Recognition Applications and Methods
Figure 5: Training & validation losses of model training
with different network settings: (a). CTC loss with an
LSTM-CTC model (Belay et al., 2019b). (b) CTC loss with
a CNN-LSTM-CTC model (Belay et al., 2020). (c) CE loss
of attention based encoder-decoder model. (d) CTC loss of
the proposed blended attention-CTC model.
dropout rate of 0.25, on top of seven convolutional
layers that are stacked serially with ReLU activa-
tion function as an encoder. In the attention based
encoder-decoder model, the decoder has a unidirec-
tional LSTM with 128 hidden units while in the
blended attention-CTC approach, the decoder LSTM
is removed from the previous model and then the
CTC objective function that are blended with atten-
tion mechanism is in place.
The convolutional layers of the encoder module is
composed of seven convolutional layers which have a
kernel size of 3 × 3, except that the one on top is with
a 2× 2 kernel size, four max-pooling layers with pool
sizes of 2 for the first pooling layer, and 2 × 1 for the
remaining pooling layers. Strides are fixed to one, and
the ‘same’ padding is used in all convolutional layers.
The number of feature maps are 64, 128, 256, 256,
512, 512, 512 from bottom to top layers.
We use a batch size of 128 with Adam optimizer
and each model, Attention based Encoder-Decoder
(AED) and BACTC, is trained for 10 and 15 epochs
respectively. The attention based encoder-decoder
model minimizes a categorical-cross entropy (CE)
loss while the blended attention-CTC model tried to
minimize the CTC-based loss described in Equation
(4). Once the probability of labels obtained from the
trained models, we use best path decoding (Graves,
2008) to generate a character (C
) that has the maxi-
mum score at each time step t.
= argmax
|x),for t = 1,2,...,T (6)
We implement both model with Keras Application
Program Interface (API) on a TensorFlow backend.
The learning loss of the proposed model and other
models trained using different network settings are
depicted in Figure 5.
5.2 Results
The performance of the proposed blended-attention
model is evaluated against three test datasets, and
compared it with state-of-the-art approaches. Table 1,
presents the details of experimental results with Char-
acter Error Rate (CER) and the proposed model im-
proves the recognition performance by 0.67–7.50%
from the original paper (Belay et al., 2019b) and by
0.16–0.52% from the recent published paper which
use the same test dataset (Belay et al., 2020). CER is
computed, using Equation (7), by counting the num-
ber of characters inserted, substituted, and deleted in
each sequence and then dividing by the total number
of characters in the ground truth (Belay et al., 2019b).
CER(P,T) =
× 100, (7)
where q is the total number of target character labels
in the ground truth, P and T are the predicted and
ground-truth labels, and D(n,m) is the edit distance
between sequences n and m.
Figure 6: Sample text-line image with the corresponding
predicted and ground-truth texts. Characters, in the pre-
dicted text, marked with red rectangle are wrongly pre-
dicted by both Attention based Encoder-decoder(AED) and
Blended Attention-CTC( BACN).
We also implemented the attention based encoder-
decoder model, without the CTC network, and the
performance of this model is evaluated with the three
ADOCR test datasets. The blended attention-CTC
model outperforms the attention based encoder de-
coder model by 24.09%.
A Blended Attention-CTC Network Architecture for Amharic Text-image Recognition
Table 1: Comparison of test results (CER).
#test-set image-type Font-type CER (%)
Addis (Addis et al., 2018)
12 pages printed - 2.12%
Belay (Belay et al., 2019b) 2,907 Printed Power Geez 8.54%
Belay (Belay et al., 2019b) 9,245 Synthetic Power Geez 4.24%
Belay (Belay et al., 2019b) 6,479 Synthetic Visual Geez 2.28%
Belay (Belay et al., 2020) 2,907 Printed Power Geez 1.56%
Belay (Belay et al., 2020) 9,245 Synthetic Power Geez 3.73%
Belay (Belay et al., 2020) 6,479 Synthetic Visual Geez 1.05%
Ours 2,907 Printed Power Geez 1.04%
Ours 9,245 Synthetic Power Geez 3.57%
Ours 6,479 Synthetic Visual Geez 0.93%
* Denotes methods tested on different datasets.
As we observed the empirical results, the atten-
tion based encoder-decoder model implemented with-
out CTC, becomes poor when the sequence length
increases. In most cases, the first 4 to 6 characters
are always correctly predicted while the rest errors
have no any patterns. Such character errors are not
observed in the blended attention-CTC model. In
summary, the proposed blended attention-CTC model
outperforms all the state-of-the-art models on the
ADOCR test datasets.
In this paper, we have introduced a blended attention-
CTC network called BACTC for Amharic text-line
image recognition. The proposed method consists of
a Bidirectional LSTM, stacked on top of CNN lay-
ers, as an encoder and a CTC layer as a decoder.
To enhance the hidden layer feature representation,
the attention mechanism is embedded between the
LSTM and CTC network layers without changing the
CTC objective function and the training process. All
the encoder, attention and CTC modules are trained
jointly from end-to-end.
We evaluated our model with both synthetically
generated and printed Amharic text-line images and
a significant improvement is achieved on all the three
ADOCR test datasets compared with state-of-the-art
model results. Thus, we can conclude that the blended
attention-CTC network is more effective for Amharic
text image recognition than widely used attention-
based encoder-decoder and CNN-LSTM-CTC based
networks as well. This work can be potentially
extended and applied for handwritten Amharic text
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