## 1 Introduction  
Amharic is the second-largest Semitic dialect in the world after Arabic [1]. It is an official working language of the Federal Democratic Republic of Ethiopia and is spoken by more than 50 million people as their mother language and by over 100 million as a second language in the country [2,3]. In addition to Ethiopia, it is also spoken in other countries like Eritrea, USA, Israel, Sweden, Somalia, and Djibouti [1,4]. Dated back to the 12 th century, many historical and literary documents in Ethiopia are written and documented using Amharic script. Amharic is a syllabic writing system which is derived from an ancient script called Geez, and it has been extensively used in all government and non-government sectors in Ethiopia until today. Amharic took all of the symbols in Geez and added some new ones that represent sounds not found in Geez [5]. In Amharic script, there are about 317 different alphabets including 238 core characters, 50 labialized characters, 9 punctuation marks, and 20 numerals which are written and read, as in English, from left to right [1,6]. All vowels and labialized characters in Amharic script are derived, 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. It also includes the addition of small diacritics, such as strokes or loops/circles to the right, left, top, or bottom of the character. Due to these small modifications on the consonants, Amharic characters have similar shapes which may make the task of recognition hard for machines as well as humans [7]. These features are particularly interesting in research on character recognition because a small change in the basic physical features may affect the orthographic identities of letters. The shapes and structural formations of consonant Amharic characters with their corresponding vowels and labialized variants are depicted in Figure 1.  
Optical Character Recognition (OCR) applications have been widely used and implemented for decades to digitize various historical and modern documents including books, newspapers, magazines, and cultural and religious archives that are written with different scripts. Multiple intensive works--for multiple scripts--have been done in the area of document image analysis with a better recognition accuracy; most of the scripts now even have commercial off-the-shelf OCR applications. In such a way, many researchers think that the OCR challenge is solved. However, OCR gives better results only for very specific use cases and there are still multiple indigenous scripts, like Amharic, which are underrepresented in the area of natural language processing (NLP) and document image analysis [8]. Until recent times, the OCR for Amharic script remained relatively unexplored, and it is still challenging [9; 10]. Nowadays, Amharic document processing and preservation is given much attention by many researchers from the fields of computing, linguistics, and social science [3; 11; 12; 13]. In recent years, various models and algorithms have been proposed by many researchers for image pattern recognition, and there has been a rapid advancement of solutions which have demonstrated ground-breaking performance [14]. The first work on Amharic OCR was done by [PERSON] in 1997 [15]. Since then, attempts for Amharic OCR have also been made by employing different machine learning techniques. Here, we will try to cover the various techniques considered by different researchers. A tree classification scheme with the topological features of a character was used by [PERSON] [15]. It was only able to recognize an Amharic character written with the Washera font and 12 point type. Then, other attempts were made, such as typewritten [16], machine-printed [1], and Amharic braille document image recognition [17], Geez script written on vellum [18], Amharic document image recognition and retrieval [19], numeric recognition [20], and handwritten recognition [21].