Received: 10 August 2025; Revised: 27 December 2025; Accepted: 27 December 2025; Published Online: 29 December 2025.
J. Inf. Commun. Technol. Algorithms Syst. Appl., 2025, 1(3), 25316 | Volume 1 Issue 3 (December 2025) | DOI: https://doi.org/10.64189/ict.25316
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Spell Checker for Low-resource Konkani Language
Annie Rajan,
1,*
Nehal Kalita
2
and Ambuja Salgaonkar
3
1
Department of Computer Science, DCT’s Dhempe College of Arts and Science, Panaji, Goa, 403001, India
2
Independent Researcher, Navi Mumbai, Maharashtra, India
3
Department of Computer Science, University of Mumbai, Mumbai, Maharashtra, 400098, India
*Email: ann_raj_2000@yahoo.com (A. Rajan)
Abstract
A spell checker is an application that identifies misspelled words by analyzing the sequence of characters in each
word. Spell checking applications exist for many of the Indian languages in the Eighth Schedule of the Indian
Constitution. However, there are not as many spell checkers for languages that were added later, some of which are
low-resource languages. Konkani is one such language. This is the first time a spell checker has been developed for
Konkani, in Devanagari script. Konkani is a macrolanguage, and developing a spell checker is challenging. We have
presented the design and implementation of the spell checker. The proposed approach makes use of dictionary
lookup to identify correct words and minimum edit distance to suggest correct words for misspelled words. This spell
checker also achieved a high F-score after being tested with a set of Konkani words. It has 1,510,514 unique words in
the dictionary.
Keywords: Natural language processing; Indian language; Diacritics; Minimum edit distance; Python.
1. Introduction
India is a country with 22 languages in its Eighth Schedule language list
[1]
to the constitution and 38 languages that are
not on the list. A developing country like India needs the digital footprint of these languages as various Natural
Language Processing (NLP) tools. Building NLP tools like Part-of-Speech (PoS) tagger,
[2]
Named Entity Recognizer
(NER),
[3]
morphological analyzer,
[4]
etc. is a challenging task since to build these tools there is a need for an annotated
corpus in the language in which the tool is built. Information processing in low-resource languages involves
technologies and methods to understand corpora and linguistic databases. By processing this information, the resources
of low-resource languages can be preserved, helping to bridge communication gaps.
A spell checker is an application that flags words in a document that may not be spelled correctly.
[5,6]
A spell checker
is a basic need of a word processor in any language. The spell checker analyzes the written text in order to identify any
misspellings and gives the best correct suggestions for those misspellings. Spell checking applications present valid
suggestions to the user based on each misspelled word encountered in the user’s document. The user then selects from
a list of suggestions or chooses to ignore the suggestions and accept the current word as valid. Regardless of how often
this is done, the spell-checking application will perform its task independent of the types of misspelled words most
commonly made by the user. The spell checkers are crucial in making quality content without any mistakes or
ambiguity. A misspelled word can change the meaning, focus, and intention of a word and therefore its content, and
also can lead to reading and attention discomfort.
The essence of digital applications is exponentially increasing day by day. It is difficult to imagine a regular day
without search engines, social media, online news, emails, and word processing. Further, there are other NLP
applications like speech-to-text and text-to-speech engines, Optical Character Recognition (OCR) systems, speech
synthesizers, and Machine Translation (MT) systems that are evolving. Spell checkers and correctors play a crucial
role in the development of all these applications, and they are deeply coupled with the NLP ecosystem. Extensive work
is reported for English spelling detection and a limited number of Indian languages, whereas no work has been reported
for Konkani, the state language of Goa, India. Konkani is a low-resource macrolanguage that has multiple scripts and
is classified in the linguistic database ISO 639-3.
[7]
The Konkani language has 36 consonants and 12 vowels. The
methods available for other languages cannot be directly applied to Konkani. One such example is phonetic based spell
checking which cannot be directly implemented in a generalized Konkani spell checker. This is because the
pronunciation differs among different variants of Konkani, like Mangalorean Konkani differs Goan Konkani. In this
paper, we have tried to develop a spell checker for Konkani with 1,510,514 unique words.
[8]
1.1 Literature survey
Spelling checkers are the basic tools needed for word processing and document preparation. It is a tool that enables
users to check the spellings of the words in a text file, validates them, i.e., checks whether they are right or wrongly
spelled, and, in case the spell checker has doubts about the spelling of the word, suggests possible alternatives. As
mentioned in Ref. 12, spell checkers look for four types of possible errors: a wrong letter (‘byll’), an inserted letter
(‘buall’), an omitted letter (‘bul’), or a pair of adjacent transposed letters (‘blul’) (considering the correct word to
be ’bull’). These types of errors can be resolved by a dictionary lookup.
Languages like Hindi,
[9-13]
Tamil,
[14−18]
Punjabi,
[19-21]
Kashmiri,
[22]
Sanskrit,
[23]
Bengali,
[24-28]
Marathi,
[29,30]
Gujarati,
[31,32]
Sindhi,
[33-35]
Telugu,
[13,36]
Kannada,
[37,38]
Malayalam,
[39-44]
Urdu
[45-48]
Assamese,
[49-51]
Manipuri,
[52]
Nepali.
[53-55]
Oriya,
[56,57]
and Bodo,
[58]
have papers in spell checker. In Table 1, the methods used in papers on spell checking for Indian languages
have been listed.
Table 1: Methods used in papers of spell checking and suggestion.
Sr.
No.
Language
Methods
Train data
size in
words
Test data
size in
words
Overall
accuracy
Ref.
1
Hindi
Minimum edit distance, Statistical machine
translation
*
870
83.2%
[9]
2
Hindi
Character n-gram, Dictionary lookup
*
*
*
[10]
3
Hindi
Dictionary lookup, Minimum edit distance
~117,000
291
*
[11]
4
Hindi
Minimum edit distance
*
*
*
[12]
5
Hindi
Statistical machine translation, Convolutional neural
network and Gated recurrent unit
108,587
*
85.4%
[13]
6
Tamil
Character bi-gram, Minimum edit distance, Word
frequency, Hashing
4,000,000
2,105
98.4%
[14]
7
Tamil
Bloom filter, Minimum edit distance, Long short-
term memory
249,056
*
*
[15]
8
Tamil
Dictionary lookup, Character bi-gram, Minimum
edit distance
*
*
89.13%
[16]
9
Tamil
Minimum edit distance, Distance matrix
*
*
*
[17]
10
Tamil
Minimum edit distance, Rule-based, Soundex, Long
short-term memory
*
*
95.67%
[18]
11
Punjabi
Lexicon lookup
~150,000
225
95.61%
[19]
12
Punjabi
Lexicon lookup
*
*
83.5%
[20]
13
Punjabi
Dictionary lookup, Minimum edit distance
~1,000,000
*
87.2%
[21]
14
Kashmiri
Dictionary lookup, Hashing, Binary search tree
~1,000,000
*
~80%
[22]
15
Sanskrit
Morphological rules
13,000
1,500
99%
[23]
16
Bangla
Partition around medoids clustering
*
2,450
99.8%
[24]
17
Bangla
Dictionary lookup, Minimum edit distance
15,162,317
250,000
~95%
[17]
18
Bangla
Double metaphone encoding
*
1,607
91.67%
[26]
19
Bangla
Finite state automation
*
*
~70%
[27]
20
Bangla
Convolutional neural network, Bidirectional encoder
representations from transformers model
513,000
52,400
87.8%
[28]
21
Marathi
Morphological rules
13,000
10,648
99.57%
[29]
22
Marathi
Minimum edit distance, Cosine similarity algorithm
*
929,663
85.88%;
86.76%
[30]
23
Gujarati
Minimum edit distance
192,000
79,024
83.14%
[31]
24
Gujarati
Mel frequency cepstral coefficients, Gammatone
frequency cepstral coefficients, Bidirectional
encoder representations from transformers model
*
*
*
[32]
25
Sindhi
Minimum edit distance, SoundEx algorithm,
ShapeEx algorithm
100,000+
*
*
[33]
26
Sindhi
Minimum edit distance, SoundEx algorithm,
ShapeEx algorithm
250,000
1,744
*
[34]
27
Sindhi
Dictionary lookup
70,576
2,052
*
[35]
28
Telugu
Statistical machine translation, Convolutional neural
network and Gated recurrent unit
92,716
*
89.3%
[13]
29
Telugu
Markov Models
3,300,000+
*
*
[36]
30
Kannada
Morphological rules, Dictionary lookup
*
112,000
97.83%
[37]
31
Kannada
Morphological rules, Dictionary lookup
3,000,000
43,209
90% noun;
80% verb
[38]
32
Malayalam
Long short-term memory
1,505,279
500
55.2%
[39]
33
Malayalam
Character n-gram, Minimum edit distance
~80,000
10,000
91%
[15]
34
Malayalam
Finite state machine
*
*
*
[41]
35
Malayalam
Dictionary lookup, Character n-gram
~10,000
10,000
*
[42]
36
Malayalam
Sequence-2-sequence, Lexicon lookup, Hashing,
Character n-gram
10,000
6,000
91.26%
[43]
37
Malayalam
Word length difference, Minimum edit distance,
Character n-gram, Phoneme similarity, Random
forest classifier
*
*
71%
[44]
38
Urdu
SoundEx algorithm, Single edit distance
1,700,000
724
96.27%
[45]
39
Urdu
SoundEx algorithm, ShapeEx algorithm
1,700,000
280
93.5%
[46]
40
Urdu
Reverse edit distance
110,582
*
*
[47]
Sr.
No.
Language
Methods
Train data
size in
words
Test data
size in
words
Overall
accuracy
Ref.
41
Urdu
Character n-gram, Minimum edit distance
593,738
510,547
83.67%
[48]
42
Assamese
Dictionary lookup, SoundEx algorithm, Hashing,
Minimum edit distance
*
5,000+
*
[49]
43
Assamese
Minimum edit distance, Morphological rules,
Dictionary lookup
16,000
1,000;
5,000;
10,000
0.58; 0.63;
0.69 recall
[50]
44
Assamese
Character tri-gram
220,743
500
76.6%
[51]
45
Manipuri
Dictionary lookup, Reverse dictionary lookup,
Minimum edit distance, Phonetic encoding
~10,000
*
*
[52]
46
Nepali
Character bi-gram, Bidirectional encoder
representations from transformers model,
Probabilistic spelling correction
~350,000
~125,000
69.1%
[53]
47
Nepali
Dictionary lookup, Minimum edit distance, Decision
Tree
*
*
78%
[54]
48
Nepali
Gated recurrent unit
120,000
*
73%
[55]
49
Odia
Confusion matrix, Character n-gram
*
685
88%
[56]
50
Odia
Dictionary lookup, Minimum edit distance
36,000
*
*
[57]
51
Bodo
Morphological rules, Dictionary lookup
~15,000
~1,500,000
*
[58]
* Values not listed
Currently, there are no papers available on spell checking for the Konkani language. The proposed spell-checking
approach discussed in this study is mainly based on dictionary lookups and minimum edit distance.
[59]
2. Methodology
The Konkani language has 15 diacritics.
[60]
A diacritic is a mark added to a letter to show a change in pronunciation,
tone, or emphasis. In Konkani, diacritics in the Devanagari script help distinguish sounds. For example, in the word
(kā
), the nasal diacritic (anusvara) above (ā) creates a nasalized ‘a’ sound, setting it apart from (kā), which
lacks this nasal quality. In this spell checker, the character count of these diacritics is set to 0.5, and the count of other
alphabets is set to 1. Fig. 1 shows the workflow of the proposed approach.
Fig. 1: Flowchart of the Konkani spell checker.
2.1 Dataset
The data used to generate our dictionary originated from the author of Ref. 3, supplemented by a collection of ~4,000
additional words, totaling 1,510,514 words.
2.2 Dictionary creation
The dictionary developed for the spell checker has the words arranged in sequential order based on their first character,
words having the same first character are further arranged in ascending order of length. Three comma separated valued
files were created and were used as reference for executing the proposed approach.
The first file, File-1, is the dictionary, where each row consists of the Konkani words and their respective character
count in the aforementioned order. A sample demonstration is shown in Table 2. In the second file, File-2, each row
consists of unique alphabets, the beginning and ending row numbers in File-1 starting with the current alphabet, and
the minimum and maximum character count of the words starting with the alphabet. A sample demonstration is shown
in Table 3. The third file, File-3, contains the range of rows in File-1 covered by words beginning with each alphabet,
where the range is further subdivided as per character count. A sample demonstration is shown in Table 4, where
columns like ‘4.0 start’ and ‘4.0 end’ denotes the beginning and ending rows in File-1, that has words with the specified
character count. The value -1 for the Konkani alphabet, means that there are no words with lengths 4.0, 4,5 and 5.0
that begins with this alphabet.
Table 2: Sample rows from File-1.
Row no.
Word
Character count
0
2.0
1
2.0
295189
1.5
334068
8.0
334069
1.5
Table 3: Sample range of rows in File-2.
Alphabet
Start row no.
End row no.
Min. character count
Max. character count
0
60449
2.0
8.0
295189
334068
1.5
8.0
Table 4: Sample range of rows in File-3.
Alphabet
4.0 start row no.
4.0 end row no.
4.5 start row no.
4.5 end row no.
5.0 start row no.
5.0 end row no.
189436
191986
191987
197007
197008
204162
296000
297169
297170
299281
299282
302316
-1
-1
-1
-1
-1
-1
Although File-2 contains less information than File-3, this file reduces computation time to identify whether an input
word is incorrect based on its stored list of character count range for each alphabet. If an input word has character
count beyond the specified range in File-2, then it is declared incorrect. Whereas if the character count is within the
range, then File-3 is referred for further analysis of correctness.
2.3 Pipeline of code
Table 5 shows the various abbreviations used while explaining the pipeline of code. The workings of our code are
briefly explained with the help of the five steps mentioned below.
(1) Check if w
i
can be traced in File-1 by iterating through the range of words having the same α as w
i
. If it is
traceable, then further steps should not be continued.
(2) Identify w
s_l
based on w
i_l
.
If w
i_l
is 1.5 or 2.0, then w
s_l
can be within range → (1.0, 2.0); if w
i_l
is 3.0, then w
s_l
can be within range → (2.0,
4.0); if w
i_l
is 3.0, then w
s_l
can be within range → (2.0, 4.0); if w
i_l
is 3.5, then w
s_l
can be within range → (2.5, 4.5);
if w
i_l
is 4.0, then w
s_l
can be within range → (3.0, 5.0); if w
i_l
is > 4.0, then w
s_l
can be within range → ( ((ceiling
value of w
i_l
) * 0.8), (w
i_l
/ 0.8) ).
(3) Compare w
i
with words from File-1 whose length is within the range of w
s_l
.
If w
i_l
is > 4.0, then s
p
is 80; if w
i_l
is 4.0, then s
p
is 75; if w
i_l
is 3.0, then s
p
is 66.66; if w
i_l
is 1.5 or 2.0, then s
p
is 50
(if s
c
is 1.0) or 75 (if s
c
> 1.0).
(4) Sort w
s
as per s
p
in descending order.
(5) Display the sorted w
s
.
Table 5: List of abbreviations.
Sr. No.
Abbreviations
Full form
1
w
i
input word
2
α
first alphabet of a word
3
w
i_l
length of input word
4
w
s
suggested words
5
w
s_l
expected length of suggested words
6
w
d
word from dictionary as per w
s_l
7
s
p
final minimum similarity value of w
i
and w
s
in percentage
8
s
c
initial count of similar characters between w
i
and w
d
in sequence
9
w
ln
the longer word among w
i
and w
d
10
w
st
the shorter word among w
i
and w
d
If the list of w
s
contains s
p
values > 80, then display the first ten w
s
as most preferred suggestions and the remaining
as other probable suggestions.
In step (3), the calculation of similarity in terms of the comparison is done in two steps.
The first step is to determine s
c
. There are two methods, and these are demonstrated below with the help of pseudocode.
Method A:
Method B:
In method A, the counter for w
st
is not updated if the current character of w
st
is not equal to the current character of w
ln
.
This method is useful for a wrong input word like ‘friiend’, and a correct word to be suggested is ‘friend’. Since
‘friiend’ is the longer word and the character ‘i’ is repeated, the counter of this word can be incremented, whereas the
counter for the correct and shorter word ‘friend’ can remain unchanged when iterating through the second ‘i’.
In method B, the counters for both w
st
and w
ln
are incremented in every iteration, regardless of the equality of
characters. This method is useful for a wrong input word like ‘friendey’, and a correct word to be suggested is
‘friendly’. Only the characters in position 7 (‘e’ and ‘l’) of both the words do not match, but the succeeding characters
in position 8 (‘y’) do. Detecting suggestions for this wrong word through method A would lead to a lesser value of s
c
as only the first six characters would be identified as similar. So, the larger value among s
c1
and s
c2
is considered the
value of s
c
.
The second step of the calculation of similarity is to determine s
p
. For this, the formula (s
c
/ length of w
ln
) * 100 is
used, and the value obtained is compared with different conditions of percentage values depending on w
i_l
. If conditions
are satisfied, then that value is considered the value of s
p
.
2.4 Spell checker tool
The Konkani Spell Check (https://konkanispellcheck.pythonanywhere.com/) tool has been designed using a Python
(version 3.5) based web framework, Django (version 2.2). The Konkani dictionary is parsed using the ’csv’ library.
The tool did not rely on any database management system. Its user interface incorporates responsive web design, so it
changes based on the screen resolution. Fig. 2 shows the output of words getting analyzed by the application. When a
s
c1
= 0 // s
c
value by method A
c
ln
= 0 // counter for w
ln
c
st
= 0 // counter for w
st
while (c
ln
< length of w
ln
) {
if (c
st
< length of w
st
) and (w
st
[c
st
] == w
ln
[c
ln
])
{
update s
c1
as per conditions
increment c
st
by 1
increment c
ln
by 1
}
else {
increment c
ln
by 1
}
}
s
c2
= 0 // s
c
value by method B
c
ln
= 0 // counter for w
ln
c
st
= 0 // counter for w
st
while (c
ln
< length of w
ln
) {
if (c
st
< length of w
st
) and (w
st
[c
st
] == w
ln
[c
ln
])
{
update s
c2
as per conditions
}
increment c
st
by 1
increment c
ln
by 1
}
user enters a misspelled word, the tool flags it with ‘/W’ in the user-input text box and tries to suggest some correct
alternatives in the suggested-words text box.
Fig. 2: Analysis through Konkani spell check tool.
3. Results and discussion
A test dataset of 4,853 unique Konkani words from books of Standards 1, 2, and 3 by SCERT-Goa
[61]
was analysed,
out of which 4,041 words were identified as correct by our dictionary. Out of 812 unidentified words, 522 words were
assigned suggestions by our system. The F-score value in Table 7 is calculated as per the confusion matrix outcomes
shown in Table 6. The analysis of this dataset took 4,600.15 seconds on a local machine with an Intel Core i7-3770
CPU and 1600 MHz RAM. The analysis of only the 4,041 identified words was significantly faster, taking 605.77
seconds. The algorithm analyzes correct words faster because it doesn't need to perform word identification for
suggestion.
Table 6: Confusion matrix.
Outcome
Value
True Positive (TP)
4,041
False Positive (FP)
0
True Negative (TN)
0
False Negative (FN)
812
Table 7: Classification metrics.
Metric
Formula
Value
Precision (P)
TP / (TP + FP)
1
Recall (R)
TP / (TP + FN)
0.833
F-score
2 * (P * R) / (P + R)
0.909
Table 8 presents a comparison of different incorrect word inputs for a proper noun, (gəɳəpət
ə). Since this noun
is of length 4.0 units, the expected s
p
is 75%. Hence, in Fig. 2, it is shown as a suggestion for 1
st
and 2
nd
input words
by the application.
Table 8: Comparison of incorrect inputs.
Sr. no.
Input
Length
Similarity (in %)
1
(gəɳɑːpət
ə)
4.5
88.88
2
(gəɳəpəɳə)
4.0
75
3
(gəɳəpət
ət
ɑː)
5.5
72.72
4
(gət
ɑːpət
ə)
4.5
66.66
4. Conclusion
The results demonstrate that our spell checker achieved an F-score of 0.909 on a dataset consisting of words from
school books of Standards 1, 2 and 3. This makes our spell checker a reliable source for checking basic Konkani words.
The implementation of an alphabet and word length-based arrangement of the dictionary makes this spell checker
much faster in giving suggestions to wrong words than a conventional spell checker where no such arrangements are
implemented in its dictionary. Future experiments can emphasize on implementing a much faster algorithm for correct
word suggestions and increasing the size of the corpus. A bigger corpus can help a spell checker reliable in checking
advanced Konkani words, the ones that are used in school books of higher standards.
Data Availability Statement
The test data is available in a Harvard Dataverse repository (https://doi.org/10.7910/DVN/ECWMBB).
Conflict of Interest
There is no conflict of interest.
Supporting Information
Not applicable
Use of artificial intelligence (AI)-assisted technology for manuscript preparation
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing
or editing of the manuscript and no images were manipulated using AI.
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