Parsing Hindi tables better

[Gautam-Rajeev]

Description

We are currently struggling to parse Hindi tables to be able to replicate the table into structured useful form (like json/.md) and need help figuring out bonding boxes for each cell for detected table.

Sample pdfs for testing :

1. Pdf1
2. Pdf2

Test case : merged_tables.pdf

Implementation Details

1. Detection if the page has a table and separating the table image from the rest of the page : This is done using Table Transformers - This works pretty well on most tested use cases. We need to set the detection confidence pretty high (more than 99% ) and its able to detect if the page has the table and the correct boundary for it with reasonable accuracy. Code to set this up is here

2. Detection of the text in the table : "

This we have seen the best results using Pytesseract Hindi. In our limited testing, we have notices that Pytesseract works best we give the page in its entirety rather than after detection and separation of rows/column/cells. Something like this which gives the word as well as the bounding boxes :

from PIL import Image
import pytesseract
from pytesseract import Output

# Load the image from file
image_path = 'table.png'
image = Image.open(image_path)
tesseract_config = '-l hin --psm 6 --oem 3'

data = pytesseract.image_to_data(image, config=tesseract_config, output_type=Output.DICT)

# Iterate over each word detected and print the word along with its bounding box
n_boxes = len(data['text'])
for i in range(n_boxes):
    if int(data['conf'][i]) > 0: 
        (x, y, w, h) = (data['left'][i], data['top'][i], data['width'][i], data['height'][i])
        word = data['text'][i]
        print(f"Word: {word}, Coordinates: ({x}, {y}, {w}, {h})")

3. Detection of the cells/ bounding boxes of the cell: This is what we require focus on.
   We tried using Table Transformers for this but its giving poor results. Any logic for OCR heuristics (column boundaries can be identified by ability to draw a straight vertical line without hitting a word) would seem more plausible as a first solution here than fine-tuning image transformers. Ideal initial step would be a step in this direction.

Sample pdfs show different kinds of tables (some without columns marked by lines, others without lines demarcating the columns.

Guidelines for proposed solutions :

• Anyone is free to work on proposed solutions to complete the 3rd aspect (detection of bounding boxes for each row/column/cell ) and integrate with the rest of the outputs.
• People submitting working PRs will be considered favorably for internship applications in the future.
• Please submit code which is quickly replicable on our end- github repos should have setup to install all required libraries and a folder with inputs and output. Even collabs one can easily run on my end to see results are good enough(again please install any necessary packages within it). Code quality and optimization are unimportant for now, just need something that works.

Product Name

AI Tools

Organization Name

SamagraX

Domain

NA

Tech Skills Needed

Pytorch/ Python, ML

Category

Feature

Mentor(s)

@GautamR-Samagra

Complexity

High

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Please update the ticket

[35C4n0r]

@GautamR-Samagra I would like to work on this issue, kindly assign it to me.

[Gautam-Rajeev]

@35C4n0r Thanks for showing interest :)
Anyone can work on this, I am not assigning to anyone in particular now. Please raise PRs - just link it to any working code.

[Amit0617]

Is this the requirement? https://colab.research.google.com/drive/1pCYJ4H9vwV3GoriQ6UNoIj8N42_i3tMv?usp=sharing Before (upload it as image.png in colab to test)

After (a bit rough marking of start(top) and end(bottom) of line.)

[Amit0617]

After few more changes

[Gautam-Rajeev]

@Amit0617 this looks great. We also need to be able to detect the columns too. Then we need to merge the output of bbox of these with the bbox extracted from the text using pytesseract.

[Gautam-Rajeev]

Hi, I'm planning to have a meeting today (22-Jan-2024) to discuss progress on this and clarify any doubts at 5:30 pm. Let me know if you can join @Amit0617 @35C4n0r @SarveshAtawane on this link

[Amit0617]

Oops! I missed it.

[naina35]

suggestions...

[Gautam-Rajeev]

suggestions...

Hi! The goal is to detect the column and rows and not the cells. i.e. I should be finally able to tell that these are the elements in the same column and these in the same row and convert to a csv. Do check if your approach is able to do that

[35C4n0r]

@GautamR-Samagra Here are the results and my collab notebook

{
    0: {
        0: "",
        1: "गी तिथि",
        2: "की तिथि",
        3: "अवधि (दिनों में)",
        4: "क्षमता कु. / हे.",
        5: "",
    },
    1: {
        0: "पूसा डबल जीरो-3॥",
        1: "206",
        2: "",
        3: "442",
        4: "23.3",
        5: "यो फोर्टिफाइड प्रजाति- इरयू  [सिड 2 प्रतिशत से कम  लूकोसाइनोलेटस 30 पीपीएम से कम)",
    },
    2: {
        0: "पूसा सरसों-29 (एल.ई.टी.-3(",
        1: "))2043",
        2: "",
        3: "43-55",
        4: "49.27-25.68",
        5: "रयूसिक एसिड की मात्रा बहुत न्यून",
    },
    3: {
        0: "पूसा सरसों-24 (एल.ई.टी.-8",
        1: ") 2008",
        2: "",
        3: "440",
        4: "20.25",
        5: "'रयूसिक एसिड की मात्रा बहुत न्यून।",
    },
    4: {
        0: "पूसा सरसों-2",
        1: "2007",
        2: "",
        3: "433-42",
        4: "48.6-2.0",
        5: "_रयूसिक एसिड की मात्रा बहुत न्यून |",
    },
    5: {0: "(एल.ई.एस--27)", 1: "", 2: "", 3: "", 4: "", 5: ""},
    6: {0: "त्त क्षेत्रों के लिए", 1: "", 2: "", 3: "", 4: "", 5: ""},
    7: {
        0: "वभव",
        1: "985",
        2: "48.4.85",
        3: "25-30",
        4: "45-20",
        5: "म्पूर्ण उ.प्र. हेतु",
    },
    8: {
        0: "वरूणा (टा. - 59)",
        1: "4975",
        2: "2.2.76",
        3: "420-25",
        4: "45-20",
        5: "म्पूर्ण मैदानी क्षेत्र हेतु",
    },
    9: {
        0: "आर.जी.एन.-298",
        1: "2045",
        2: "",
        3: "443",
        4: "शा",
        5: "[ल की मात्रा अधिक।",
    },
    10: {
        0: "गिरिराज (डीआरएमआरयू34)",
        1: "20]3",
        2: "",
        3: "437-53",
        4: "22.46-श27.57",
        5: "ल की मात्रा अधिक।",
    },
    11: {
        0: "पूसा सरसों-27",
        1: "20॥]",
        2: "",
        3: "408-35",
        4: "44.37-6.59",
        5: "रयूसिक एसिड की मात्रा बहुत न्यून |",
    },
    12: {0: "पन्\u200dत पीली सरसों 4", 1: "2040", 2: "", 3: "407-43", 4: "42-5", 5: ""},
    13: {0: "पीताम्बरी", 1: "2009", 2: "", 3: "440-5", 4: "44.7-7.65", 5: "ड़ा दाना।"},
    14: {0: "(आर.वाइ.एस.क. 05-02)", 1: "", 2: "", 3: "", 4: "", 5: ""},
    15: {
        0: "एन.आज.सी.एच.बी.-0॥",
        1: "2009",
        2: "",
        3: "405-35",
        4: "43.82-4.9]",
        5: "",
    },
    16: {0: "इसे बुआई के लिए", 1: "", 2: "", 3: "", 4: "", 5: ""},
    17: {
        0: "आशीर्वाद",
        1: "2005",
        2: "26.08.05",
        3: "430-35",
        4: "20-22",
        5: "म्पूर्ण उ.प्र. हेतु",
    },
    18: {
        0: "वरदान",
        1: "985",
        2: "48.4.85",
        3: "420-25",
        4: "48-20",
        5: "म्पूर्ण उ.प्र. हेतु",
    },
    19: {0: "/ लवणीय भूमि हेतु", 1: "", 2: "", 3: "", 4: "", 5: ""},
    20: {
        0: "नरेन्द्र राई",
        1: "990",
        2: "47.08.90",
        3: "",
        4: "",
        5: "म्पूर्ण उ.प्र. हेतु",
    },
    21: {
        0: "सी.एस.-52",
        1: "4987",
        2: "45.05.98",
        3: "435-45",
        4: "46-20",
        5: "म्पूर्ण उ.प्र. हेतु",
    },
    22: {
        0: "सी.एस.-54",
        1: "2003",
        2: "42.02.05",
        3: "435-45",
        4: "48-22",
        5: "म्पूर्ण उ.प्र. हेतु",
    },
    23: {0: "प्रजातियाँ :", 1: "", 2: "", 3: "", 4: "", 5: ""},
    24: {
        0: "जे.के. सम्रद्धि गोल्ड",
        1: "206",
        2: "",
        3: "425-30",
        4: "20.0-30.0",
        5: "फेद रस्ट एवं डाउनी मिल्डयू सहिष्णु",
    },
    25: {0: "(जे.के.एम.एस.-2)", 1: "", 2: "", 3: "", 4: "", 5: ""},
    26: {
        0: "जे.के. पुखराज",
        1: "206",
        2: "",
        3: "445-20",
        4: "45.0-20.0",
        5: "फेद रस्ट एवं डाउनी मिल्डयू सहिष्णु",
    },
    27: {0: "(जे.के.वाई.एस.-2)", 1: "", 2: "", 3: "", 4: "", 5: ""},
    28: {
        0: "बेयर सरसों - 5450",
        1: "206",
        2: "",
        3: "430-35",
        4: "28.0-30.0",
        5: "ल की मात्रा अधिक |",
    },
    29: {0: "अलबेली 4", 1: "2045", 2: "", 3: "440-445", 4: "2.3", 5: ""},
}

for the table

I've also added the logic for converting it to a Python dictionary

[Gautam-Rajeev]

merged_tables.pdf Looks good @35C4n0r Can you try on the above PDF? I have combined multiple table types into one pdf, lets make sure the solution works for all of them. If it does, this should be good enough.

cc: @Amit0617 @SarveshAtawane @naina35

[35C4n0r]

@GautamR-Samagra Sorry for the late response, I ran the script for all the tables, The results are not ok in some cases, for example the table on page 22 and this is one of the cells extracted (not complete)

There are many more cases like this, you can check the notebook.

[35C4n0r]

@GautamR-Samagra What should be my next step, have you checked the notebook?

[Gautam-Rajeev]

@35C4n0r can you manually estimate for how many its working out of 26 tables?

Can you verify if its working for all the tables which don't have row/column line boundaries ?

[Gautam-Rajeev]

@35C4n0r Got time to go through your code now. Table transformer is not doing a good enough job detecting rows and columns properly. However I think the overall approach is very useful and we can extend it to plug and play with any DETR and pytesseract combination. Let's do that as a seaparate ticket. Goal will be to improve detection of text objects and you can use any DETR model along with Pytesseract.

You provide the pdf, image as input along with the DETR model and the Pytesseract-language You get as output, each word in each page labelled as the DETR object .. with words combined wherever they belong to the same object

For example here , you get as output : the word of each cell mapped to row1/column1/cell1/table1 another word to row2/column1/cell4/table1 and so on.

Can you create another ticket on ai-tools with these details and assign it to yourself. I'll add to the details. I'll decide the community points etc.

Once the performance of table transformers or any other object detection model improves, we can use this is conjunction with that

[Gautam-Rajeev]

Since, this is not being done with the above approaches, Let's try and solve this from ground up heuristically to just try to pass the test cases on the pdf. Then we'll abstract the ideas to make it generic and useful.

Have a bunch of ideas here..
Key idea is that if the tables have lines for rows and column, just detect the lines and use that. If not, use other approaches.

• For each page detect which part is the table using table transformer and separate it out (I'm happy with its object detection capabilities of the table). Let's use the code above.
• As done in above code, keep detect all the words and the bbox of each word through PyTesseract and keep it
• For the table image, let's detect the lines and all the words in each line and keep it. (not rows yet, line detection is simpler than row detection)
• Let's detect the horizontal lines on the page. Remove any horizontal lines that are intersecting with the bbox of the words (words that have been detected as lines)
• We need to decide if the table image had horizontal border-lines for rows now- we devise a rule based on number of lines and number of 'border-lines' - it could be as simple as if the table has number of border-lines > 2, then use those to detect the rows
• If the table doesnt have border lines- then find the max horizontal gap between the words on adjacent rows (from pystesseract words) . We'll consider the average gap as the gap which determines row-borderlines
• Similarly, detect the column vertical borderlines if any
• // Place holder here-- how to detect the columns if borders are not there (vague idea around figuring out the gap between horizontally adjacent words and using the 2nd most-common gap (assuming first most common gap is just space between words))
• Create the borders -- remove the lines wherever its intersecting with the words bbox from Pytesseract
• Expand the outer boundaries to edge of the image (i.e. if the first row boundary upper boundary should be top edge, last column should be right edge etc)
• Combine everything to get the word-row/cell/column mapping

We can keep adding ideas around models around row /column detection and figure out how to ensemble them all

cc: @Amit0617 @SarveshAtawane @naina35

[35C4n0r]

@GautamR-Samagra, sorry for the huge delay, got some time today and made a little bit of progress, Here is the notebook https://colab.research.google.com/drive/1Z5H_Wp-UdfC75UXJGIqm3cDpTiCpgt91?usp=sharing

I've used the classic Canny Edge Detection + Hugh Transform, and the results are awesome. What I got stuck at is the Pytesseract's OCR capabilities, my config: -l hin+eng

What I would like to have, is something like @naina35's solution, If possible, kindly share your approach with us @naina35. Or we use some other OCR models or DETR, suggestions, and ideas are welcome (@GautamR-Samagra, @Amit0617, @naina35)

Once I have the bboxes for the text objects, I'll be able to clean up the Edge detection.

[Gautam-Rajeev]

Hey @35C4n0r can you pause work on this for now. I have made some progress myself and will clean and update that here in a couple of hours.

[35C4n0r]

Sure @GautamR-Samagra.

[naina35]

hey @35C4n0r this was my first time trying my hand with python so I couldn't do much usually my tech domain is different

this was what i managed to do import cv2 import numpy as np import matplotlib.pyplot as plt

file = 'C:/Users/hp5cd/Desktop/parse hindi tables/hindi5.jpg'
im = cv2.imread(file)
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)

ret, thresh_value = cv2.threshold(im_gray, 180, 255, cv2.THRESH_BINARY_INV)

kernel = np.ones((2, 5), np.uint8) dilated_value = cv2.dilate(thresh_value, kernel, iterations=3)

contours, hierarchy = cv2.findContours(dilated_value, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

for cnt in contours: x, y, w, h = cv2.boundingRect(cnt)

if y < 1000:
    cv2.rectangle(im, (x, y), (x + w, y + h), (0, 0, 255), 1)

cv2.imshow('detecttable78', im) cv2.imwrite('detecttable_with_rectangles4.jpg', im) cv2.waitKey(0) cv2.destroyAllWindows()

[Gautam-Rajeev]

@35C4n0r @naina35 sorry for the delay. Here is my effort at parsing the tables. It uses the same classic edge detection techniques you highlighted. For tables with the borderlines, it is able to detect the rows and columns with 100% accuracy in our test cases. It fails if the lines are blurry (we have one case like that). It also fails gracefully i.e when there are no columns/row lines or they are blurry, it doesn't detect either rows or columns and we can use alternative approaches in those cases.

@35C4n0r Would be very useful if you could continue with the logic you had written in line with the comment I had detailed above to extend the capabilty to tackle any table. Also, clean up code to get the table in a dataframe once we have detected the rows and columns accurately.

Also had kept all the images of the tables here for easy use and testing (after table detection) detected_pdf.zip

Cleaned up here

[Sanika-k-1317]

Hello @Shruti3004 I have experience on working with pytorch and have made a project in deep learning. I am really interested in contributing to this project. Could you please let me know how I can connect for further discussions and assign the ticket to me?

[riyasachdeva04]

Hello @Shruti3004 Can I work on this issue or is it inactive now?

[harshaharod21]

Is this task active? I was looking to contribute in projects in the domain of AI and ML.