## 1 Introduction
However, all of these researchers applied statistical machine learning techniques and limited private datasets. In addition, recognition was done at the character level which is time consuming and also may not  
Figure 1: The shapes and structural formations of sample Amharic characters: (**a**) Basic Amharic characters with the orders of consonant–vowel variants (\(34\times 7\)), including the lately introduced Amharic character (\(\mathfrak{\eta}\)). Characters in the first column are consonants and the others are derived variants (vowels) formed from each consonant by adding diacritics and/or removing part of the character, as marked with the red box. (**b**) Derived/labialized characters. Labialized characters marked with circles are also derived from consonant characters. achieve better recognition accuracy, since each image of a character is directly dependent on the nature of the segmentation algorithms [22]. Following the success of deep learning, other attempts have also been made for Amharic character recognition by employing Convolutional Neural Networks (CNNs) [6; 8; 23]. Nearly all of these attempts performed segmentation of text images into the character level, which also directly affects the performance of the OCR. The only exceptions are [PERSON] [21] and recently published works [9; 24]. [PERSON] [21] proposed an Hidden Markov Model (HMM)-based model for offline handwritten Amharic word recognition without character segmentation by using the structural features of characters as building blocks of a recognition system, while [PERSON] [9] and [PERSON] [24] proposed Long-Short-Term Memory (LSTM) networks together with CTC (Connectionist Temporal Classification) for Amharic text image recognition. In literature, attempts at Amharic OCR have neither shown results on large datasets nor considered all possible characters used in the Amharic writing system [9]. There are many effective off-the-shelf commercial and open-source OCR applications for many languages, including the functionality of ground truth generation from the existing printed texts so as to train the second model [25]. The effectiveness of various open-source OCR engines was evaluated on 19 th century Fraktur scripts; the evaluation shows that the open-source OCR engine can outperform the commercial OCR application [26]. Based on deep neural networks, many segmentation-based [27] and segmentation-free OCR techniques have been studied. Impressive progress has also been made for many Latin and non-Latin scripts, ranging from historical handwritten documents to modern machine-printed texts. Bidirectional Recurrent Neural Networks with Long-Short-Term Memory (LSTM) architecture for online handwriting recognition [28], Convolutional Recurrent Neural Networks (CRNN) for Japanese handwriting recognition [29], Urdu Nastaleeq script recognition using bidirectional LSTM [30], segmentation-free Chinese handwritten text recognition [31], a hybrid Convolutional Long-Term Memory Network(CLSTM) for text image recognition [32], Multidimensional LSTM (MDLSTM) for Chinese handwriting recognition [33], combined Connectionist Temporal Classification (CTC) with Bidirectional LSTM (BLSTM) for unconstrained online handwriting recognition [34], MDLSTM for handwriting recognition [35], an online handwritten mathematical expression recognition using a Gated Recurrent Unit (GRU)-based attention mechanism [36], a combination of Convolutional Neural Networks and multi-dimensional RNN for Khmer historical handwritten text recognition [37], and a multi-stage HMM-based text recognition system for handwritten Arabic [38] have been studied. However, OCR systems for many scripts, especially those which are indigenous to the African continent, such as Amharic, remain under-researched, and none of the researchers have taken advantage of deep learning techniques such as end-to-end learning, used for many languages, for developing Amharic OCR.