Not able to extract Textract merge cell text properly

Not able to extract the merge cell text properly. There is some issue with combine headers function. Textract not able to extract the top header text properly.

Reference:
t_doc = TDocumentSchema().load(textract_json)
ordered_doc = order_blocks_by_geo(t_doc)
trp_doc = Document(TDocumentSchema().dump(ordered_doc))
Now let’s iterate through the tables’ content, and extract the data into a DataFrame:

table_index = 1
dataframes = []
def combine_headers(top_h, bottom_h):
bottom_h[3] = top_h[2] + " " + bottom_h[3]
bottom_h[4] = top_h[2] + " " + bottom_h[4]
for page in trp_doc.pages:
for table in page.tables:
table_data = []
headers = table.get_header_field_names() #New Table method to retrieve header column names
if(len(headers)>0): #Let's retain the only table with headers
print("Statememt headers: "+ repr(headers))
top_header= headers[0]
bottom_header = headers[1]
combine_headers(top_header, bottom_header) #The statement has two headers. let's combine them
for r, row in enumerate(table.rows_without_header): #New Table attribute returning rows without headers
table_data.append([])
for c, cell in enumerate(row.cells):
table_data[r].append(cell.mergedText) #New Cell attribute returning merged cells common values
if len(table_data)>0:
df = pd.DataFrame(table_data, columns=bottom_header)

Document table format:

with above logic:

With small changes in the combine header, my issue got solved to some extent:

```
def combine_headers(top_h, bottom_h):
for i in range(len(top_h)):
if bottom_h[i] != top_h[i]:
bottom_h[i] = top_h[i] + ' ' + bottom_h[i]
else :
bottom_h[i] = bottom_h[i]
```

But there is some issue with textract top header detection,

Start by tracing the table header path around get_header_field_names(), combine_headers(), and Cell.mergedText in the response parser. Reproduce the supplied Textract table example and compare the detected top and bottom headers with the screenshots. Done means merged-cell text and multi-row headers are extracted consistently, with regression coverage for the reported case.

Written by the indexing model from the issue text.